Publikationsliste der Universität Kassel

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.

Universität und wissenschaftliches Publizieren gehören zusammen. Dabei kommt dem Publikationsmanagement an Hochschulen immer mehr an Bedeutung zu. Die Interessenlage ist vielschichtig. Autoren wünschen komfortable Arbeitsumgebungen, die Hochschulpräsidien benötigen Forschungsinformationen, und Bibliotheken stellen mit Reposi-torien Infrastrukturen zur Verfügung. Neue Trends im Bibliothekswesen bringen weitreichende Veränderungen technologischer und organisatorischer Art. Ausgehend von den Bedürfnissen von Autoren wurde an der Universität von Kassel ein Projekt namens PUMA aufgesetzt, um die verschiedenen Interessen in einer modernen Umgebung zusam-menzuführen. Innerhalb von PUMA finden Autoren neben einem Social-Bookmarking- und Bibliographie-System eine automatische Weitergabe ihrer Metadaten an Forschungs-informationssysteme, Repositorien und E-Learning-Systeme.

Repositorien stellen heute einen festen Bestandteil der Bibliothekslandschaft dar. Fast jede Bibliothek verfügt über ein Institutional Repository, sei es selbst installiert oder gehostet. Die jeweils benutzte Repositoriensoftware ist gemessen an der Zahl der Systeme übersichtlich, jedoch in den Funktionen und Ansätzen durchaus unterschiedlich. In der letzten Zeit gibt es einiges an Bewegung. Sehr deutlich ist dies in den entsprechenden Repository-Verzeichnissen statistisch ablesbar. Die Zahl der stetig weiterentwickelten Softwaresysteme verringert sich, einige Systeme werden über den eigentlichen Repositorybereich ausgebaut (z.B. zu Forschungsinformationssystemen) oder schliessen sich zusammen. Auch in Deutschland lassen sich diese Veränderungen erkennen. Ziel der Blockveranstaltung auf dem Bibliothekartag war, eine Bestandsaufnahme der wichtigsten Repositorien-Systeme zu leisten und die heutige und zukünftige Entwicklung dieser Systeme zu skizzieren. Berichtet wurde über den Entwicklungsstand der Systeme DSpace, EPrints, eSciDoc/Fedora, MyCoRe und OPUS.

Wissenschaftliche Kommunikation ist durch das Internet zwar viel leichter geworden, sie lebt aber dennoch auch davon, dass Menschen sich nicht nur per E-Mail, sondern im persönlichen Dialog verständigen. Wenn Sie also in der scientific community zu Hause sind und die Idee haben, Fachkolleginnen und Fachkollegen einzuladen oder wenn Sie gefragt werden, ob Sie nicht die nächste Konferenz an Ihrer Hochschule ausrichten wollen, dann soll Ihnen diese kleine Broschüre dabei helfen.

Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system.

The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

In less than two decades, mobile communication has developed from a niche application to a mass-market high-tech product, having experienced an unprecedented growth never achieved by any other technology, whether radio, television, or even the Internet.

Welchen Stellenwert hat die vom Kunden wahrgenommene Innovativität von Unternehmen auf dem Gesundheitsmarkt? Die Studie erweitert das Strukturmodell des American Customer Satisfaction Index und vergleicht länderübergreifend in Finnland, Deutschland, Spanien und Russland den Einfluss von Innovativität auf Kundenzufriedenheit und Reputation des Lieferanten. Die Ergebnisse zeigen, dass die wahrgenommene Innovativität in drei der vier untersuchten Kulturen einen hohen Stellenwert besitzt.

The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.

The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.

Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures

Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags are used for navigation, finding resources, and serendipitous browsing and thus provide an immediate benefit for the user. By now, several systems for tagging photos, web links, publication references, videos, etc. have attracted millions of users which in turn annotated countless resources. Tagging gained so much popularity that it spread into other applications like web browsers, software packet managers, and even file systems. Therefore, the relevance of the methods presented in this thesis goes beyond the Web 2.0. The conceptual structure underlying collaborative tagging systems is called folksonomy. It can be represented as a tripartite hypergraph with user, tag, and resource nodes. Each edge of the graph expresses the fact that a user annotated a resource with a tag. This social network constitutes a lightweight conceptual structure that is not formalized, but rather implicit and thus needs to be extracted with knowledge discovery methods. In this thesis a new data mining task – the mining of all frequent tri-concepts – is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. Extending the theory of triadic Formal Concept Analysis, we provide a formal definition of the problem, and present an efficient algorithm for its solution. We show the applicability of our approach on three large real-world examples and thereby perform a conceptual clustering of two collaborative tagging systems. Finally, we introduce neighborhoods of triadic concepts as basis for a lightweight visualization of tri-lattices. The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by our research group. Besides being a useful tool for many scientists, it provides interested researchers a basis for the evaluation and integration of their knowledge discovery methods. This thesis introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features. Furthermore, BibSonomy is used as foundation for evaluating and integrating some of the discussed approaches. Collaborative tagging systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In this thesis we evaluate and compare several recommendation algorithms on large-scale real-world datasets: an adaptation of user-based Collaborative Filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag co-occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag co-occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We demonstrate how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. Furthermore, we show how to integrate recommendation methods into a real tagging system, record and evaluate their performance by describing the tag recommendation framework we developed for BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. We also present an evaluation of the framework which demonstrates its power. The folksonomy graph shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Clicklogs of web search engines can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large folksonomy snapshot and on query logs of two large search engines. We find that all of the three datasets exhibit similar structural properties and thus conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of collaborative tagging users is driven by similar dynamics. In this thesis we further transfer the folksonomy paradigm to the Social Semantic Desktop – a new model of computer desktop that uses Semantic Web technologies to better link information items. There we apply community support methods to the folksonomy found in the network of social semantic desktops. Thus, we connect knowledge discovery for folksonomies with semantic technologies. Alltogether, the research in this thesis is centered around collaborative tagging systems and their underlying datastructure – folksonomies – and thereby paves the way for the further dissemination of this successful knowledge management paradigm.

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The two-volume set LNCS 6769 + LNCS 6770 constitutes the proceedings of the First International Conference on Design, User Experience, and Usability, DUXU 2011, held in Orlando, FL, USA in July 2011 in the framework of the 14th International Conference on Human-Computer Interaction, HCII 2011, incorporating 12 thematically similar conferences. A total of 4039 contributions was submitted to HCII 2011, of which 1318 papers were accepted for publication. The total of 154 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on DUXU theory, methods and tools; DUXU guidelines and standards; novel DUXU: devices and their user interfaces; DUXU in industry; DUXU in the mobile and vehicle context; DXU in Web environment; DUXU and ubiquitous interaction/appearance; DUXU in the development and usage lifecycle; DUXU evaluation; and DUXU beyond usability: culture, branding, and emotions.

Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.

The present volume features a selection of the papers presented at the 9th International Conference on Formal Concept Analysis (ICFCA 2011). Over the years, the ICFCA conference series has grown into the premier forum for dissemination of research on topics from formal concept analysis (FCA) theory and applications, as well as from the related fields of lattices and partially ordered structures. FCA is a multi-disciplinary field with strong roots in the mathematical theory of partial orders and lattices, with tools originating in computer science and artificial intelligence. FCA emerged in the early 1980s from efforts to restructure lattice theory to promote better communication between lattice theorists and potential users of lattice-based methods for data management. Initially, the central theme was the mathematical formalization of concept and conceptual hierarchy. Since then, the field has developed into a constantly growing research area in its own right with a thriving theoretical community and an increasing number of applications in data and knowledge processing including disciplines such as data visualization, information retrieval, machine learning, software engineering, data analysis, data mining, social networks analysis, etc. ICFCA 2011 was held from May 2 to May 6, 2011, in Nicosia, Cyprus. The program committee received 49 high-quality submissions that were subjected to a highly competitive selection process. Each paper was reviewed by three referees (exceptionally two or four). After a first round, some papers got a definitive acceptance status, while others got accepted conditionally to improvements in their content. The latter got to a second round of reviewing. The overall outcome was the acceptance of 16 papers as regular ones for presentation at the conference and publication in this volume. Another seven papers have still been assessed as valuable for discussion at the conference and were therefore collected in the supplementary proceedings. The regular papers presented hereafter cover advances on a wide range of subjects from FCA and related fields. A first group of papers tackled mathematical problems within the FCA field. A subset thereof focused on factor identification within the incidence relation or its lattice representation (papers by Glodeanu and by Krupka). The remainder of the group proposed characterizations of particular classes of ordered structures (papers by Doerfel and by Meschke et al.). A second group of papers addressed algorithmic problems from FCA and related fields. Two papers approached their problems from an algorithmic complexity viewpoint (papers by Distel and by Babin and Kuznetsov) while the final paper in this group addressed algorithmic problems for general lattices, i.e., not represented as formal contexts, with an FCA-based approach (work by Balcázar and Tîrnăucă). A third group studied alternative approaches for extending the expressive power of the core FCA, e.g., by generalizing the standard one-valued attributes to attributes valued in algebraic rings (work by González Calabozo et al.), by introducing pointer-like attributes, a.k.a. links (paper by Kötters), or by substituting set-shaped concept intents with modal logic expressions (paper by Soldano and Ventos). A fourth group focused on data mining-oriented aspects of FCA: agreement lattices in structured data mining (paper by Nedjar et al.), triadic association rule mining (work by Missaoui and Kwuida) and bi-clustering of numerical data (Kaytoue et al.). An addional paper shed some initial light on a key aspect of FCA-based data analysis and mining, i.e., the filtering of interesting concepts (paper by Belohlavek and Macko). Finally, a set of exciting applications of both basic and enhanced FCA frameworks to practical problems have beed described: in analysis of gene expression data (the already mentioned work by González Calabozo et al.), in web services composition (paper by Azmeh et al.) and in browsing and retrieval of structured data (work by Wray and Eklund). This volume also contains three keynote papers submitted by the invited speakers of the conference. All these contributions constitute a volume of high quality which is the result of the hard work done by the authors, the invited speakers and the reviewers. We therefore wish to thank the members of the Program Committee and of the Editorial Board whose steady involvement and professionalism helped a lot. We would also like to acknowledge the participation of all the external reviewers who sent many valuable comments. Kudos also go to EasyChair for having made the reviewing/editing process a real pleasure. Special thanks go to the Cyprus Tourism Organisation for sponsoring the conference and to the University of Nicosia for hosting it. Finally we wish to thank the Conference Chair Florent Domenach and his colleagues from the Organization Committee for the mountains of energy they put behind the conference organization process right from the beginning in order to make it a total success. We would also like to express our gratitude towards Dr. Peristianis, President of the University of Nicosia, for his personal support.

1. Both direct and indirect competition can have profound effects on species abundance and expansion rates, especially for a species trying to strengthen a foothold in new areas, such as the winter moth (Operophtera brumata) currently in northernmost Finland. There, winter moths have overlapping outbreak ranges with autumnal moths (Epirrita autumnata), who also share the same host, the mountain birch (Betula pubescens ssp. czerepanovii). Competitive interactions are also possible, but so far unstudied, are explanations for the observed 1–3 years phase lag between the population cycles of the two moth species. 2. In two field experiments, we studied host plant-mediated indirect inter-specific competition and direct interference/exploitation competition between autumnal and winter moths. The experimental larvae were grown either with the competing species or with the same number of conspecifics until pupation. Inter-specific competition was judged from differences in pupal mass (reflecting lifespan fecundity), larval development time and larval survival. 3. Larval performance measurements suggested that neither direct nor indirect inter-specific competition with the autumnal moth reduce the growth rate of winter moth populations. Winter moths even had a higher probability of survival when reared together with autumnal moths. 4. Thus, we conclude that neither direct nor indirect inter-specific competition is capable of suppressing the spread of the winter moth outbreak range and that both are also an unlikely cause for the phase lag between the phase-locked population cycles of the two moth species.

The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.

The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.

The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.

The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.

The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

High technical know-how is needed to automate a manually conducted task. During the system design process the main focus is usually put on the engineering part, but the involvement of users, their knowledge, workflow, and mental models are rarely considered. The resulting technical system is fully functional, but insufficiently accepted by the users. As an example of user involvement in requirements analysis, we present a conceptual design of a system with autonomous mobile robots for inspection of gas pipes in large technical plants. In addition to user observation and task analysis, a questionnaire for elicitation of user requirements has been developed. The questionnaire inquires the operators’ knowledge and flow of work during the inspection planning and conducting, the impact of environmental parameters, subjective difficulties, and anticipation of advantages of using the system. The resulting implications for the development and design of human-computer-interface as well as human-robot-interaction are introduced.

Die Studie testet in Australien, Finnland, Russland, Spanien und Deutschland neben Faktoren wie der Weiterempfehlungsbereitschaft und Kundenzufriedenheit den Einfluss von unterschiedlichen Direktmarketing –Medien auf eine positive Unternehmensreputation. Basierend auf einem Strukturmodell werden unter Einbeziehung der Hofstede Kulturparameter Unterschiede in der Wirkung von Direktmarketing-Medien auf die Unternehmensreputation im B-to-B Marketing empirisch nachgewiesen.

One of the unresolved questions in studies on population dynamics of forest Lepidoptera is why some populations at times reach outbreak densities, whereas others never do. Resolving this question is especially challenging if populations of the same species in different areas or of closely-related species in the same area are considered. The present study focused on three closely-related geometrid moth species, autumnal Epirrita autumnata, winter Operophtera brumata and northern winter moths Operophtera fagata, in southern Finland. There, winter and northern winter moth populations can reach outbreak densities, whereas autumnal moth densities stay relatively low. We tested the hypothesis that a lower vulnerability to pupal predation may explain the observed differences in population dynamics. The results obtained do not support this hypothesis because pupal predation probabilities were not significantly different between the two genera within or without the Operophtera outbreak area or in years with or without a current Operophtera outbreak. Overall, pupal predation was even higher in winter and northern winter moths than in autumnal moths. Differences in larval predation and parasitism, as well as in the reproductive capacities of the species, might be other candidates.

In diesem Beitrag wird die Evaluation der Gebrauchstauglichkeit und Vernetzungswirkung der prototypisch umgesetzten mobilen sozialen Software Social Link mittels einer adaptierten Heuristischen Evaluation beschrieben. Social Link unterstützt seine Anwender dabei, Informationen über sich, den eigenen Standpunkt und das vorherrschende Wetter an Mitglieder einer Peer Group zu verschicken. Für die Evaluation wurden Nielsens Web-Heuristiken für die Anwendung auf mobile soziale Applikationen angepasst. Die Software wurde von sechs Evaluatoren bewertet und Optimierungsvorschläge für die Weiterentwicklung von Social Link generiert.

Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.

Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

Driven by the emergence of mobile and ubiquitous computing there is a growing demand for context-aware applications that can dynamically adapt to their runtime environment. Middleware support for providing such adaptation to mobile applications has been an attractive research and development issue for several years now. However, one of the major challenges to support adaptation is that they can not be always foreseen during the design time. In a ubiquitous computing environment, a number of mobile devices running adaptive applications supported by the instances of the same middleware may come and go in a particular adaptation domain in an unanticipated manner. Moreover, third party services may also appear and disappear with respect to that particular adaptation domain. In componentbased application architectures, with the enhancement of integrating services, mobile applications may benefit from using the components that are provided by other users as well as using third party services. At runtime the application is composed of the components and the services available within the adaptation domain. This thesis provides an approach, including theoretical background, an adaptation middleware, an MDA-based application development methodology and a tool chain, in order to solve such unanticipated adaptation problems.

The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BibTeX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very well with the state of the art methods and show an extremely high performance on consistent reference sections.

Soziale Tagging-Systeme gehören zu den in den vergangenen Jahren entstandenen Web2.0-Systemen. Sie ermöglichen es Anwendern, beliebige Informationen in das Internet einzustellen und untereinander auszutauschen. Je nach Anbieter verlinken Nutzer Videos, Fotos oder Webseiten und beschreiben die eingestellten Medien mit entsprechenden Schlagwörtern (Tags). Die damit einhergehende freiwillige Preisgabe oftmals persönlicher Informationen wirft Fragen im Bereich der informationellen Selbstbestimmung auf. Dieses Grundrecht gewährleistet dem Einzelnen, grundsätzlich selbst über die Preisgabe und Verwendung seiner persönlichen Daten zu bestimmen. Für viele Funktionalitäten, wie beispielsweise Empfehlungsdienste oder die Bereitstellung einer API, ist eine solche Kontrolle allerdings schwierig zu gestalten. Oftmals existieren keine Richtlinien, inwieweit Dienstanbieter und weitere Dritte diese öffentlichen Daten (und weitere Daten, die bei der Nutzung des Systems anfallen) nutzen dürfen. Dieser Artikel diskutiert anhand eines konkreten Systems typische, für den Datenschutz relevante Funktionalitäten und gibt Handlungsanweisungen für eine datenschutzkonforme technische Gestaltung.

Soziale Tagging-Systeme gehören zu den in den vergangenen Jahren entstandenen Web2.0-Systemen. Sie ermöglichen es Anwendern, beliebige Informationen in das Internet einzustellen und untereinander auszutauschen. Je nach Anbieter verlinken Nutzer Videos, Fotos oder Webseiten und beschreiben die eingestellten Medien mit entsprechenden Schlagwörtern (Tags). Die damit einhergehende freiwillige Preisgabe oftmals persönlicher Informationen wirft Fragen im Bereich der informationellen Selbstbestimmung auf. Dieses Grundrecht gewährleistet dem Einzelnen, grundsätzlich selbst über die Preisgabe und Verwendung seiner persönlichen Daten zu bestimmen. Für viele Funktionalitäten, wie beispielsweise Empfehlungsdienste oder die Bereitstellung einer API, ist eine solche Kontrolle allerdings schwierig zu gestalten. Oftmals existieren keine Richtlinien, inwieweit Dienstanbieter und weitere Dritte diese öffentlichen Daten (und weitere Daten, die bei der Nutzung des Systems anfallen) nutzen dürfen. Dieser Artikel diskutiert anhand eines konkreten Systems typische, für den Datenschutz relevante Funktionalitäten und gibt Handlungsanweisungen für eine datenschutzkonforme technische Gestaltung.

Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise??, and (iii) in learning ontologies.

Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise�?, and (iii) in learning ontologies.

Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world socialbookmarking application BibSonomy. The results indicate that the evidencenetworks reflect the relative rating of the explicit ones very well.

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

Je komplizierter technische Geräte werden, desto mehr Ärger können sie bei ihren Benutzern verursachen. Es gibt wohl kaum jemanden, der nicht schon einmal gern seinen PC aus dem Fenster geworfen oder eigenhändig zertrümmert hätte. Wenige, die nicht bei der Installation eines neuen DVD-Rekorders kurz vor der Kapitulation standen. Hilfe kommt nun verstärkt aus der psychologischen Forschung, die sich intensiv damit beschäftigt, wie Apparate anwenderfreundlich und klug gestaltet werden können.

Im vorliegenden Beitrag wird die Notwendigkeit einer eindeutigen Autorenidentifikation (Persistant Author Identification - PAI) diskutiert und die bisher vorhandenen fachlichen, nationalen, überregionalen und kommerziellen Ansätze vorgestellt und erläutert. Es zeigt sich, dass die Entwicklung auf dem Sektor der Personennormdateien (authorithy records) mittels VIAF und ISNI auf einem guten Wege ist. Bei der notwendigen Zusammenführung der bisherigen Autoren-Identifikatoren in einem übergeordneten System könnte der neuen ORCID-Initiative große Bedeutung zukommen.

An den Hochschulen kommt der Förderung einer zeitgemäßen Publikationsumgebung steigende Bedeutung zu. Die Interessenlage ist vielschichtig. Autoren möchten eine komfortable Arbeitsumgebung, die Hochschulpräsidien benötigen Forschungsinformationen und die Bibliotheken wollen passende Informationsinfrastrukturen bereitzustellen. An der Universität Kassel wurde ausgehend vom Bedürfnis des wissenschaftlichen Autors in einem Pilotprojekt (PUMA) versucht, diese Interessen zu einem Publikationsmanagement zu vereinbaren. Für den Autor wird in PUMA bei deutlich geringerem Einsatz ein erheblicher Mehrwert dadurch generiert, dass bei nur einmaligen Anmelden mit dem Bibliotheks-Account ein Social-Bookmarking-System (BibSonomy) zur Verfügung steht, automatisiert Informationen an den Forschungsbericht weitergegeben, Metadaten und Dateien in Repositorien gestellt und Schriftenverzeichnisse (Curriculum Vitae) für Homepages etc. erstellt werden können.

The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning and sequence labeling approaches like Conditional Random Fields (CRF) are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set.

Plants respond to grazing by herbivorous insects by emitting a range of volatile organic compounds, which attract parasitoids to their insect hosts. However, a positive outcome for the host plant is a necessary precondition for making the attractionbeneficial or even adaptive. Parasitoids benefit plants by killing herbivorous insects, thus reducing future herbivore pressure,but also by curtailing the feeding intensity of the still living, parasitised host. In this study, the effect of parasitismon food consumption of the 5th instar larvae of the autumnal moth (Epirrita autumnata) was examined under laboratory conditions. Daily food consumption, as well as the duration of the 5th instar, was measuredfor both parasitised and non-parasitised larvae. The results showed that parasitism by the solitary endoparasitoid Zele deceptor not only reduced leaf consumption significantly but also hastened the onset of pupation in autumnal moth larvae. On the basisof the results, an empirical model was derived to assess the affects on the scale of the whole tree. The model suggests thatparasitoids might protect the tree from total defoliation at least at intermediate larval densities. Consequently, a potentialfor plant–parasitoid chemical signalling appears to exist, which seems to benefit the mountain birch (Betula pubescens ssp. czerepanovii) by reducing the overall intensity of herbivore defoliation due to parasitism by this hymenopteran parasitoid.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

Auditory information from the situation around us, available in open nature is lost in vehicles. To maintain the three-dimensional picture and situational awareness, pilots in an aircraft cockpit need to interpret two-dimensional data provided on flat screens. The visual information prevails; the auditory channel is used mainly for alerting or getting attention. In an ongoing research project we evaluate means to provide information using audio signals. For these audio signals, carrying information about state, direction, and amount of deviation explicit sound design is essential. Even when using voice commands, where the information is put into the words chosen, parameters exist that can support or oppose to this semantic information, like pitch (low to relax, high to alert), personality (robotic for regular automatic updates, human for unexpected trustworthy information). The paper shows, how an existing flight instrument is mapped to an audio display and what principles from psychology and music are applied to sound design.

Business processes and their implementation as Web Service Compositions are not only dependent on Web Services and partners all over the Internet, but also on their failsafe execution. Service providers have to obligate their services to perform according to negotiated Quality of Service (QoS) parameters. For example, response time and throughput are important parameters to achieve fast and efficient services. Overloaded or failing services may compromise the reliability and execution of whole enterprise processes.\ \ In this paper we introduce a flexible monitoring approach for the measurement of QoS in BPEL (Business Process Execution Language) processes. We propose a generic algorithm for QoS aggregation in BPEL processes. The novel generic aggregation algorithm applies customized aggregation functions for QoS dimensions. Furthermore, we present a BPEL monitoring system which supports ad-hoc sensor deployment and efficient runtime and offline data aggregation not only for whole process descriptions but also sections inside service processes.

Neuartige Spielesteuerungen, die auf dem Prinzip eines Brain-Computer Interface basieren, können möglicherweise auch für andere Computeranwendungen sinnvoll genutzt werden. Der Beitrag stellt ein solches Eingabegerät und die Ergebnisse eines ersten Usabilitytests vor.

Fragmentation, deterioration, and loss of habitat patches threaten the survival of many insect species. Depending on their trophic level, species may be differently affected by these factors. However, studies investigating more than one trophic level on a landscape scale are still rare. In the present study we analyzed the effects of habitat size, isolation, and quality for the occurrence and population density of the endangered leaf beetle Cassida canaliculata Laich. (Coleoptera: Chrysomelidae) and its egg parasitoid, the hymenopteran wasp Foersterella reptans Nees (Hymenoptera: Tetracampidae). C. canaliculata is strictly monophagous on meadow sage (Salvia pratensis), while F. reptans can also parasitize other hosts. Both size and isolation of habitat patches strongly determined the occurrence of the beetle. However, population density increased to a much greater extent with increasing host plant density ( = habitat quality) than with habitat size. The occurrence probability of the egg parasitoid increased with increasing population density of C. canaliculata. In conclusion, although maintaining large, well-connected patches with high host plant density is surely the major conservation goal for the specialized herbivore C. canaliculata, also small patches with high host plant densities can support viable populations and should thus be conserved. The less specialized parasitoid F. reptans is more likely to be found on patches with high beetle density, while patch size and isolation seem to be less important.

Generalist natural enemies are usually not considered as being capable of causing population cycles in forest insects, but they may influence the population dynamics of their prey in the low density cycle phase when specialist enemies are largely absent. In the present field study, the total response of the generalist invertebrate predator community to experimentally established pupal densities of the closely related autumnal (Epirrita autumnata) and winter moths (Operophtera brumata) was analysed. Due to the high amount of variation in the dataset, the exact shape of the response curve could not be convincingly estimated. Nevertheless, two important conclusions can be drawn from the analyses. Firstly, the natural invertebrate predator community seems to become saturated at rather low densities of both autumnal and winter moth pupae. Secondly, the predator community seems to become saturated at much lower densities of autumnal than of winter moth pupae. Furthermore, pupal mass was significantly negatively correlated with invertebrate predation probability in autumnal moth pupae. These results indicate that differences in the predator assemblage being able to consume pupae of the two moth species, as well as different handling times, could be responsible for the substantially higher predation rates in winter than in autumnal moth pupae. As a consequence, the population dynamics of autumnal moths might be less affected by generalist invertebrate predators than those of winter moths, as autumnal moths seem able to escape from the regulating influence of generalist predators at much lower population densities than winter moths

In the literature, various environmental factors are described as being capable of influencing the reproductive output of insect females irrespective of their body size. Still, female body size or weight is widely used as a proxy for fecundity. In the present study, a seven-year data set on the autumnal moth, Epirrita autumnata (Borkhausen) (Lepidoptera: Geometridae), was used to analyze whether the body weight-fecundity relationship in this capital breeding, cyclic forest defoliating lepidopteran is constant across years. Ambient temperature conditions and density of conspecifics during larval development, the length of the pupal period, as well as moth densities in the parent generation were examined as factors capable of modifying the body weight-fecundity relationship. While the regression slope of potential fecundity (total egg numbers per female) on pupal mass was constant across years, the mean total egg number per given body weight (the regression intercept) was significantly different between years. This residual variance in egg numbers after controlling for the effect of pupal mass was best explained by the pooled geometrid density (autumnal and winter moths) in the parent generation. The total egg number per given body weight decreased with increasing density of geometrid moths in the parent generation. Thus, maternal density effects on offspring fecundity were found in this system. Their rather weak nature suggests, however, that this maternal effect alone does not have the potential of causing cyclic population dynamics in the autumnal moth.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitungder Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitungder Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitungder Universitaet Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten fuer wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalitaet, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erlaeutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und aehnlichen Systemen enthaltenen Daten ein.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a rst evaluation of two exemplarily deployed recommendation methods.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.

Among cyclic populations of herbivores, inter-specific temporal synchrony has been attributed to both climatic factors and trophic interactions. In northern Europe, winter and autumnal moths undergo regular 9–11 year population cycles. The winter moth cycle has typically been phase-locked with that of the autumnal moth, but with a 1–3-year phase lag. We examined potential effects of natural enemies on this phase lag using field experiments and observational data. We found that larval parasitism was significantly higher in autumnal than in winter moths. Conversely, pupal predation by generalist invertebrates was clearly greater in winter than in autumnal moths. The difference in parasitism rates may contribute to the earlier collapse of the autumnal moth cycle. In addition, the phase lag may be strengthened by higher pupal mortality in winter moths in the early increase phase of the cycles. As a consequence, we put forward a hypothesis on reversed effects of natural enemies, providing a potential explanation for phase-lagged population cycles of these moth species.

In our living environments today, computer networks, computing devices, sensors and many other entities produce a large amount of data to be processed. This data comprise, for example, digital media content, news, computational resources like services, and much more. To be able to exploit this data, a (human or computer) user may utilize digital assistants. In this paper, we present such an assistant for deploying services and obtaining content data. We concentrate especially on how this assistant is able to adapt to changing situations, both, user-driven and in a self-adaptive manner. An experimental evaluation, based on real world digital video providers, demonstrates prerequisites, results, and potential of the various ways of adaptation within the presented approach.

Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

Die Bedeutung des Dienstgüte-Managements (SLM) im Bereich von Unternehmensanwendungen steigt mit der zunehmenden Kritikalität von IT-gestützten Prozessen für den Erfolg einzelner Unternehmen. Traditionell werden zur Implementierung eines wirksamen SLMs Monitoringprozesse in hierarchischen Managementumgebungen etabliert, die einen Administrator bei der notwendigen Rekonfiguration von Systemen unterstützen. Auf aktuelle, hochdynamische Softwarearchitekturen sind diese hierarchischen Ansätze jedoch nur sehr eingeschränkt anwendbar. Ein Beispiel dafür sind dienstorientierte Architekturen (SOA), bei denen die Geschäftsfunktionalität durch das Zusammenspiel einzelner, voneinander unabhängiger Dienste auf Basis deskriptiver Workflow-Beschreibungen modelliert wird. Dadurch ergibt sich eine hohe Laufzeitdynamik der gesamten Architektur. Für das SLM ist insbesondere die dezentrale Struktur einer SOA mit unterschiedlichen administrativen Zuständigkeiten für einzelne Teilsysteme problematisch, da regelnde Eingriffe zum einen durch die Kapselung der Implementierung einzelner Dienste und zum anderen durch das Fehlen einer zentralen Kontrollinstanz nur sehr eingeschränkt möglich sind. Die vorliegende Arbeit definiert die Architektur eines SLM-Systems für SOA-Umgebungen, in dem autonome Management-Komponenten kooperieren, um übergeordnete Dienstgüteziele zu erfüllen: Mithilfe von Selbst-Management-Technologien wird zunächst eine Automatisierung des Dienstgüte-Managements auf Ebene einzelner Dienste erreicht. Die autonomen Management-Komponenten dieser Dienste können dann mithilfe von Selbstorganisationsmechanismen übergreifende Ziele zur Optimierung von Dienstgüteverhalten und Ressourcennutzung verfolgen. Für das SLM auf Ebene von SOA Workflows müssen temporär dienstübergreifende Kooperationen zur Erfüllung von Dienstgüteanforderungen etabliert werden, die sich damit auch über mehrere administrative Domänen erstrecken können. Eine solche zeitlich begrenzte Kooperation autonomer Teilsysteme kann sinnvoll nur dezentral erfolgen, da die jeweiligen Kooperationspartner im Vorfeld nicht bekannt sind und – je nach Lebensdauer einzelner Workflows – zur Laufzeit beteiligte Komponenten ausgetauscht werden können. In der Arbeit wird ein Verfahren zur Koordination autonomer Management-Komponenten mit dem Ziel der Optimierung von Antwortzeiten auf Workflow-Ebene entwickelt: Management-Komponenten können durch Übertragung von Antwortzeitanteilen untereinander ihre individuellen Ziele straffen oder lockern, ohne dass das Gesamtantwortzeitziel dadurch verändert wird. Die Übertragung von Antwortzeitanteilen wird mithilfe eines Auktionsverfahrens realisiert. Technische Grundlage der Kooperation bildet ein Gruppenkommunikationsmechanismus. Weiterhin werden in Bezug auf die Nutzung geteilter, virtualisierter Ressourcen konkurrierende Dienste entsprechend geschäftlicher Ziele priorisiert. Im Rahmen der praktischen Umsetzung wird die Realisierung zentraler Architekturelemente und der entwickelten Verfahren zur Selbstorganisation beispielhaft für das SLM konkreter Komponenten vorgestellt. Zur Untersuchung der Management-Kooperation in größeren Szenarien wird ein hybrider Simulationsansatz verwendet. Im Rahmen der Evaluation werden Untersuchungen zur Skalierbarkeit des Ansatzes durchgeführt. Schwerpunkt ist hierbei die Betrachtung eines Systems aus kooperierenden Management-Komponenten, insbesondere im Hinblick auf den Kommunikationsaufwand. Die Evaluation zeigt, dass ein dienstübergreifendes, autonomes Performance-Management in SOA-Umgebungen möglich ist. Die Ergebnisse legen nahe, dass der entwickelte Ansatz auch in großen Umgebungen erfolgreich angewendet werden kann.

Augmented Reality (AR) ist eine in der praktischen Anwendung immer noch wenig verbreitete interaktive Technologie, die die reale Umwelt so mit computergenerierten Informationen bedarfsgerecht und kontextbezogen ergänzen oder bereichern (augmentieren) kann, dass für den Benutzer der Eindruck einer Verschmelzung dieser Informationen mit der wahrgenommenen Realität entsteht. Neben einem Überblick zu den damit zusammenhängenden Gestaltungsbereichen für die Mensch-Maschine-Interaktion wird ein an ISO 13407 angelehntes Vorgehen vorgestellt, das eine aufgaben- und benutzergerechte Systemgestaltung gewährleisten soll. Dieses systematische Vorgehen wird im Vortrag anhand von Anwendungsbeispielen konkretisiert. Beispielsweise wurden in der Analysephase die Anforderungen an AR-Anwendungen partizipativ und szenarienbasiert in verschiedenen Fokusgruppen erhoben, bevor sie zu Use Cases zusammengefasst wurden. Mit dieser benutzerzentrierten Perspektive werden außerdem grundlagenergonomische Fragen der Displays und Informationsdarstellung, das Usability Engineering der Benutzungsschnittstellen und systemergonomische Fragestellungen der AR-Technologienutzung in unterschiedlichen Anwendungsfeldern und Arbeitskontexten aufgegriffen.

Zur Bewertung der Gebrauchstauglichkeit von Mensch-Maschine-Schnittstellen bieten sich zahlreiche Methoden an, deren Ergebnisse als konstruktive Basis für die benutzungsbezogene Verbesserung herangezogen werden können. In diesem Beitrag wird die Methode der Blickbewegungsanalyse mit ihren zentralen Parametern als software-ergonomisches Evaluationsverfahren vorgestellt und beispielhaft für die Untersuchung und Bewertung einer webbasierten Buchungsoberfläche für Flugtickets verwendet. Zusätzlich zu Benutzerleistungsdaten (wie Ausführungszeiten und Fehler) stehen damit weitere verhaltensbeschreibende objektive Daten zur Verfügung, die durch in Benutzerbefragungen erhobene, subjektive software-ergonomische Bewertungen ergänzt werden. Durch diese Methodenkombination sollte auch der Frage nachgegangen werden, ob sich die subjektiven und objektiven Daten sinnvoll gegenseitig ergänzen, so dass im Ergebnis besser auf Benutzbarkeitsdefizite geschlossen werden kann. An der empirischen Untersuchung des Fallbeispiels nahmen 20 Probanden teil, die jeweils drei repräsentative Ticket-Buchungsaufgaben lösen mussten. Die Auswertung der Blickbewegungs- und Leistungsdaten sowie der verwendeten Fragebögen lieferte trotz des hohen Erprobungsgrades der Buchungsschnittstelle und ihrer Vorgängerversionen insgesamt zahlreiche Hinweise, die im Sinne einer formativen Evaluation für die Überarbeitung der Buchungsoberfläche genutzt werden konnten. Die Analyse identifizierte teilweise übereinstimmende Problembereiche, ein Teil der Defizite konnte jedoch nur anhand der Blickbewegungsdaten aufgedeckt werden.

Für ein Verstehen des komplexen Themas "Gesundheit" ist zunächst eine Analyse der Rahmenbedingungen und Einflussgrößen erforderlich. Ergonomische Gestaltungsfelder müssen erkannt und gesundheitsförderliche Prinzipien verstanden werden, bevor Methoden zur gesundheitsförderlichen Arbeitsgestaltung angewendet und im Unternehmen organisatorisch verankert werden können. Nicht zuletzt hat Gesundheit einen, wenn auch häufig nicht entsprechend akzeptierten, hohen Stellenwert für die Wirtschaftlichkeit des Unternehmens. Gesundheitsförderung wird so zum "wertschöpfenden" Prozess und zur Querschnittsaufgabe für alle betrieblichen Akteure. Wesentliche Rahmenbedingungen und Entwicklungstendenzen sind: (1) Rechtliche Rahmenbedingungen des Arbeits- und Gesundheitsschutzes und Regelungen der Unfallversicherungsträger mit rechtlich bindendem Charakter, (2) personenbezogene Entwicklungstendenzen wie höhere Qualifikation, höherer Stellenwert persönlicher Gesundheit, steigendes mittleres Alter der Erwerbstätigen u.a., (3) Entwicklung von Krankheitsarten und des Umfangs krankheitsbedingter Ausfälle, (4) Organisatorische Entwicklungen wie Dezentralisierung von Verantwortung, Flexibilisierung von Arbeitszeiten, Veränderungen von Aufgabeninhalten und -strukturen etc. sowie (5) Technische Entwicklungen, wie z.B. die zunehmende Anzahl von Bildschirmarbeitsplätzen oder die Integration von Automatisierungstechnologie.

Supervisory control of complex systems or processes by a human operator can be understood as a superordinated control loop of the technical system. In this procedure there are three steps: (1) the operator monitors the state of a process or system, (2) he analyses and assesses it in comparison with a desired state, and finally (3), a correction by a specific operation is planned and carried out, if necessary. In the context of military aerial surveillance, the essential identification and classification tasks of aircrafts are based on various sensors and data exchange with other military units. Numerous additional conditions (e.g. the political situation) have to be considered. To assist operators in aerial surveillance tasks as well as in time-critical situations and at a computer workstation with a high informational workload, the design of ergonomic human-computer interaction is a crucial factor. A model-based design of human-computer interaction for aerial surveillance tasks at military workplaces is described in this contribution. First, interactions are generally characterized according to an extended semiotic model containing four levels of interaction (pragmatic, semantic, syntactic, and physical). In the second step, the model is used for deriving and describing design consequences. Finally, as an example, it is illustrated how to design a user interface for aerial surveillance tasks according to the model.

Vor Kurzem hat die Spiele-Industrie erste Geräte auf den Markt gebracht, die basierend auf dem Prinzip eines Brain-Computer-Interface (BCI) Computerspiele steuern sollen. Die Hersteller versprechen einfach benutzbare Hardware, optimierte Einrichtungs- und Kalibrierassistenten und vorkonfigurierten Softwareprofile für unterschiedliche Computerspiele, die jedem Benutzer den einfachen Einstig in das Spielgeschehen und eine schnellere Interaktion als mit konventionellen Eingabegeräten ermöglichen sollen. Sind diese Geräte trotz der einfachen Sensorik auch als biophysiologisches Interface für die Mensch-Computer-Interaktion bei anderen Anwendungen benutzbar? Dieser Beitrag gibt einen Überblick über derzeit verfügbare und angekündigte Geräte und stellt mit dem Neural Impulse Actuator exemplarisch ein solches Spiele-BCI vor. Ein Usability-Test, bei dem Stärken und Schwächen mit verschiedenen Testaufgaben untersucht wurden, kommt zu dem Ergebnis, dass Interaktionsaufgaben allein mit dem untersuchten BCI erfüllbar sind und solche Systeme eine sinnvolle Ergänzung zur herkömmlichen Interaktion sein können. Gleichzeitig wurde - gemessen an den hohen Erwartungen - jedoch auch noch deutliches Optimierungspotential identifiziert. Abschließend erfolgt eine Abschätzung der weiteren Einsatzmöglichkeiten im Bereich der Mensch-Computer-Interaktion.

Cooperative behaviour of agents within highly dynamic and nondeterministic domains is an active field of research. In particular establishing highly responsive teamwork, where agents are able to react on dynamic changes in the environment while facing unreliable communication and sensory noise, is an open problem. Moreover, modelling such responsive, cooperative behaviour is difficult. In this work, we specify a novel model for cooperative behaviour geared towards highly dynamic domains. In our approach, agents estimate each other’s decision and correct these estimations once they receive contradictory information. We aim at a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and a clear operational semantic for the new model. This work encompasses a complete specification of the new language, ALICA.

This poster presents a set of hash keys for bibliographic records called bibkeys. Unlike other methods of duplicate detection, bibkeys can directly be calculated from a set of basic metadata fields (title, authors/editors, year). It is shown how bibkeys are used to map similar bibliographic records in BibSonomy and among distributed library catalogs and other distributed databases.

This poster presents a set of hash keys for bibliographic records called bibkeys. Unlike other methods of duplicate detection, bibkeys can directly be calculated from a set of basic metadata fields (title, authors/editors, year). It is shown how bibkeys are used to map similar bibliographic records in BibSonomy and among distributed library catalogs and other distributed databases.

This poster presents a set of hash keys for bibliographic records called bibkeys. Unlike other methods of duplicate detection, bibkeys can directly be calculated from a set of basic metadata fields (title, authors/editors, year). It is shown how bibkeys are used to map similar bibliographic records in BibSonomy and among distributed library catalogs and other distributed databases.

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility.\ Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions.\ The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. \ In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. \ Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Several learning tasks comprise hierarchies. Comparison with a "goldstandard" is often performed to evaluate the quality of a learned hierarchy. We assembled various similarity metrics that have been proposed in different disciplines and compared them in a unified interdisciplinary framework for hierarchical evaluation which is based on the distinction of three fundamental dimensions. Identifying deficiencies for measuring structural similarity, we suggest three new measures for this purpose, either extending existing ones or based on new ideas. Experiments with an artificial dataset were performed to compare the different measures. As shown by our results, the measures vary greatly in their properties.

Several learning tasks comprise hierarchies. Comparison with a "goldstandard" is often performed to evaluate the quality of a learned hierarchy. We assembled various similarity metrics that have been proposed in different disciplines and compared them in a unified interdisciplinary framework for hierarchical evaluation which is based on the distinction of three fundamental dimensions. Identifying deficiencies for measuring structural similarity, we suggest three new measures for this purpose, either extending existing ones or based on new ideas. Experiments with an artificial dataset were performed to compare the different measures. As shown by our results, the measures vary greatly in their properties.

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

Steht ein Mensch mit geschlossenen Augen in der freien Natur, dann kann er sich schon allein aus dem, was er hört, ein Bild der Umgebung machen. Er hört dabei dreidimensional in Richtung und Entfernung. Wir wollen dieses dreidimensionale Hörerlebnis in einer technischen Umgebung zumindest teilweise zurückgeben. Wir hoffen, dass es Piloten dabei unterstützt, ein Bild ihrer Umgebung und ihrer eigenen Position darin aufzubauen. Unsere Lösung und der Aufbau zur experimentellen Überprüfung wird im Folgenden beschrieben. With closed eyes in the nature, humans can sense the surrounding just by listening. Listening is three-dimensional providing direction and distance clues. We try to provide at least part of this perception in technical settings. We expect to support aircraft pilots' awareness of situation and own position. We describe our solution and the means for an experimental evaluation in the following paper.

Steht ein Mensch mit geschlossenen Augen in der freien Natur, dann kann er sich schon allein aus dem, was er hört, ein Bild der Umgebung machen. Er hört dabei dreidimensional in Richtung und Entfernung. Wir wollen dieses dreidimensionale Hörerlebnis in einer technischen Umgebung zumindest teilweise zurückgeben. Wir hoffen, dass es Piloten dabei unterstützt, ein Bild ihrer Umgebung und ihrer eigenen Position darin aufzubauen. Unsere Lösung und der Aufbau zur experimentellen Überprüfung wird im Folgenden beschrieben. With closed eyes in the nature, humans can sense the surrounding just by listening. Listening is three-dimensional providing direction and distance clues. We try to provide at least part of this perception in technical settings. We expect to support aircraft pilots' awareness of situation and own position. We describe our solution and the means for an experimental evaluation in the following paper.

Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.

Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.

Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.

Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

The current computing and communication services are driven by various technologies. The creation of such services is however not an easy task. The different platforms lack openness that simplifies interoperability. Service development is still mainly technical oriented, where service creation tools are meant for serving and assisting the professional developers. Service creation is not seen as a task for end users right now. In this paper we will discuss how end users could be enabled to create new services, shown on the example within a mobile context-aware service platform. This is achieved by introducing the concept of the technology agnostic approach into the process. We present the conceptual architecture and explain the idea of end-user driven service creation that can provide a solution. Several tools were designed and implemented to prove that the concepts work. With the help of semantic service description, the end users can express their service wishes and create services using a graphical interface technology agnostically. The idea was implemented in two European projects and we have identified the next steps to improve the prototypes as well as the idea. Based on several tools and an underlying platform the ideas have been tested in a development environment for mobile context-aware services. Here end-users are able to design their own services or combine existing services, which can be shared and used by other users later on.

Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.

Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Social bookmarking systems constitute an establishedpart of the Web 2.0. In such systemsusers describe bookmarks by keywordscalled tags. The structure behind these socialsystems, called folksonomies, can be viewedas a tripartite hypergraph of user, tag and resourcenodes. This underlying network showsspecific structural properties that explain itsgrowth and the possibility of serendipitousexploration.Today’s search engines represent the gatewayto retrieve information from the World WideWeb. Short queries typically consisting oftwo to three words describe a user’s informationneed. In response to the displayedresults of the search engine, users click onthe links of the result page as they expectthe answer to be of relevance.This clickdata can be represented as a folksonomyin which queries are descriptions ofclicked URLs. The resulting network structure,which we will term logsonomy is verysimilar to the one of folksonomies. In orderto find out about its properties, we analyzethe topological characteristics of th

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Für die Untersuchung ergonomischer Fragestellungen bietet sich der Einsatz berührungsloser Blickerfassungssysteme an. Am FGAN Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie wird zurzeit das System SmartEye Pro verwendet, um Kopfstellung, Blickrichtung und Bewegungen des Augenlides von Probanden in Echtzeit zu erfassen. In zwei Vorstudien wurden die Auswirkung verschiedener Kalibrierungseinstellungen auf die Trackingqualität und die Qualität der Messung von Sakkaden und Fixationen evaluiert. Es zeigte sich, dass das System geeignet ist, statistische Aussagen in Bezug auf Blickbewegungen und Lidschlussverhalten zu generieren. Jedoch ist die Bestimmung von Sakkaden- und Fixationsparametern nur eingeschränkt möglich. Unter Berücksichtigung dieser Restriktionen wird das System zurzeit in einem Verbundforschungsprojekt mit der Deutschen Sporthochschule Köln und dem Zentralen Institut des Sanitätsdienstes der Bundeswehr Koblenz zur Erfassung der psycho-physiologische Leistungsfähigkeit von Soldaten verwendet.

The paper describes two experiments for investigating the influence of different levels of camera displacement on hand-eye coordination while using a video see-through head-mounted display. During the first experiment 15 camera positions with five levels of height displacement and three levels of depth displacement were compared in four different tasks. Using a two-way ANOVA the comparison of the calculated performance characteristic values showed significant influence of height displacement on hand-eye coordination. In conclusion cameras should be placed above or below eye level, but by no more than 35 mm, in order to preserve hand-eye coordination. In the second experiment, a mirror system was used to check hand-eye coordination in an exemplary medical task allowing the cameras to be placed virtually at eye level. A significant decrease in accuracy was found while using the head-mounted display compared to direct view. Finally, the mirror system was compared to the 15 camera positions using the data from the same tasks. Significant differences in performance were found between the mirror system and eye level position as well as the position slightly below eye level. The results of the experiment provide design recommendations for developers and users of video see-through systems.

Zur Bewertung der Gebrauchstauglichkeit von Mensch-Maschine-Schnittstellen bieten sich zahlreiche Methoden an, deren Ergebnisse als konstruktive Basis für die benutzungsbezogene Verbesserung herangezogen werden können. In diesem Beitrag wird die Methode der Blickbewegungsanalyse mit ihren zentralen Parametern als software-ergonomisches Evaluationsverfahren vorgestellt und beispielhaft für die Untersuchung und Bewertung einer webbasierten Buchungsoberfläche für Flugtickets verwendet. Zusätzlich zu Benutzerleistungsdaten (wie Ausführungszeiten und Fehler) stehen damit weitere verhaltensbeschreibende objektive Daten zur Verfügung, die durch in Benutzerbefragungen erhobene, subjektive software-ergonomische Bewertungen ergänzt werden. Durch diese Methodenkombination sollte auch der Frage nachgegangen werden, ob sich die subjektiven und objektiven Daten sinnvoll gegenseitig ergänzen, so dass im Ergebnis besser auf Benutzbarkeitsdefizite geschlossen werden kann. An der empirischen Untersuchung des Fallbeispiels nahmen 20 Probanden teil, die jeweils drei repräsentative Ticket-Buchungsaufgaben lösen mussten. Die Auswertung der Blickbewegungs- und Leistungsdaten sowie der verwendeten Fragebögen lieferte trotz des hohen Erprobungsgrades der Buchungsschnittstelle und ihrer Vorgängerversionen insgesamt zahlreiche Hinweise, die im Sinne einer formativen Evaluation für die Überarbeitung der Buchungsoberfläche genutzt werden konnten. Die Analyse identifizierte teilweise übereinstimmende Problembereiche, ein Teil der Defizite konnte jedoch nur anhand der Blickbewegungsdaten aufgedeckt werden.

In der Abteilung Ergonomie und Führungssysteme des FGAN Forschungsinstituts für Kommunikation, Informationsverarbeitung und Ergonomie werden Konzepte, Methoden und Werkzeuge zur benutzerzentrierten Gestaltung von Führungs- und Führungsinformationssystemen erforscht, entwickelt und angewandt. Ziel ist die Bereitstellung der erforderlichen wissenschaftlichen und technologischen Basis für ergonomisch zweckmäßige Ausrüstungsentscheidungen im Verfahren der Bedarfsermittlung und Bedarfsdeckung. Aufbauend auf ergonomischen Anforderungsanalysen werden innovative Mensch-Maschine-Schnittstellen konzipiert, in Form von Prototypen realisiert und hinsichtlich ihrer nutzergerechten Gestaltung in Feld- und Laborstudien evaluiert. Diese methodische Herangehensweise und die verschiedenen, thematisch abgegrenzten Forschungsfelder der Abteilung werden im Folgenden im Überblick dargestellt.

Die ergonomische Gestaltung von komplexen, dynamischen Systemen, in denen Menschen und Maschinen eng zusammenarbeiten, stellt eine besondere wissenschaftliche und technische Herausforderung dar. Durch innovative Konzepte und Technologien ist es heute möglich, den Menschen sehr viel besser als noch vor wenigen Jahren zu befähigen, hochautomatisierte Systeme effektiv und effizient führen und überwachen zu können. Gestaltungsziel ist es, die überragenden Fähigkeiten des Menschen im perzeptiven, kognitiven und sensumotorischen Bereich intelligent mit teilautonomen technischen Funktionen zu verzahnen. Dabei soll der Mensch zwar von unnötigen oder gefährlichen Routineaufgaben entlastet werden, sein Situationsbewusstsein jedoch voll entfalten können. Im vorliegenden Buch stellt das FGAN Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie den aktuellen Stand von Forschung und Technik dar. Der Schwerpunkt liegt in den Anwendungsbereichen Verteidigung und Sicherheit. Die 25 wissenschaftlichen Beiträge behandeln innovative Aspekte von Mensch-Maschine-Systemen wie multimodale Interaktion, 3D-Visualisierung in Virtueller Realität und Augmented Reality, adaptive Assistenzsysteme, Head-Mounted Displays, Führung unbemannter Robotersysteme, Cognitive Engineering und Ecological Interface Design oder Evaluationsmethoden für die Mensch-Maschine-Interaktion. Angesprochen werden Ingenieure, Informatiker und Technologiemanager, die verantwortlich für die Projektierung und Entwicklung von komplexen Mensch-Maschine-Systemen sind. Auch Studierende technischer Fachrichtungen können sich mit diesem Werk einen umfassenden Überblick über den Stand von Forschung und Technik verschaffen.

Mit dem Ziel einer software-ergonomischen Anzeigegestaltung wurde an Marine-Arbeitsplätzen in einer Operationszentrale die Mensch-Maschine-Interaktion zur Luftraumüberwachung analysiert. Die Benutzerinteraktionen können mit Hilfe des Semiotischen Modells in vier Ebenen differenziert und entsprechenden hierarchischen Stufen des technischen Systems zugeordnet werden. Dieses Modell wurde dann als Gestaltungsleitlinie herangezogen und in Kooperation mit Marine-Experten ein Prototyp entwickelt. Die Modellaspekte der einzelnen Ebenen und ihre Umsetzung in der Luftraum-überwachungsanzeige werden in diesem Beitrag vorgestellt.

Dieser Beitrag beschreibt die modellbasierte Gestaltung der Mensch-Computer-Interaktion an militärischen Arbeitsplätzen zur Luftlagebewertung. Die Interaktion wird zunächst allgemein anhand eines erweiterten Semiotischen Modells auf vier Ebenen (pragmatisch, semantisch, syntaktisch und physikalisch) charakterisiert. Daraus werden die entsprechenden Gestaltungsbereiche abgeleitet und kurz beschrieben. Als exemplarische Umsetzung wird dann anhand eines Prototyps die Gestaltung einer Benutzungsschnittstelle für die Luftlagebewertung dargestellt.

In diesem Beitrag wird ein Entscheidungsleiter-Modell für die menschliche Informationsverarbeitung vorgestellt und exemplarisch für die Gestaltung von Benutzungsschnittstellen verwendet. Das hierarchisch geordnete Modell bildet Wissenszustände und Informationsverarbeitungsschritte bei der Situationsanalyse und Handlungsplanung ab und wird als Grundlage für eine kognitiv-kompatible Systemgestaltung genutzt. Durch die Unterteilung des gesamten Verarbeitungsprozesses in verschiedene kognitive Teilaufgaben lassen sich die jeweils dazu benötigten Informationen identifizieren und formulieren. Jede der im Modell repräsentierten Analyseaktivitäten verlangt nach einer speziellen Art der Darstellung der zugrundeliegenden Informationen, damit sie optimal in den erforderlichen Entscheidungsschritt eingebunden werden können. Im Einzelnen werden dazu Aspekte der Aktivierung, der Beobachtung und Identifizierung von kritischen Zuständen, der regelbasierten Ableitung von Handlungsbedarf, der Bildung von Zustandsgrößen und der wissensbasierten Analyse und Handlungsplanung aufgegriffen und anhand eines Anwendungsbeispiels konkretisiert. Dazu wurde exemplarisch eine Mensch-Maschine-Schnittstelle konzipiert und prototypisch in einer Experimental- und Testumgebung realisiert, mit deren Hilfe Prozesse der Luftraumüberwachung und Lagebewertung durch einen Operateur auf verschiedenen kognitiven Ebenen unterstützt werden sollen.

In diesem Beitrag wird ein Entscheidungsleiter-Modell für die menschliche Informationsverarbeitung vorgestellt und exemplarisch für die Gestaltung von Benutzungsschnittstellen verwendet. Das hierarchisch geordnete Modell bildet Wissenszustände und Informationsverarbeitungsschritte bei der Situationsanalyse und Handlungsplanung ab und wird als Grundlage für eine kognitiv-kompatible Systemgestaltung genutzt. Durch die Unterteilung des gesamten Verarbeitungsprozesses in verschiedene kognitive Teilaufgaben lassen sich die jeweils dazu benötigten Informationen identifizieren und formulieren. Jede der im Modell repräsentierten Analyseaktivitäten verlangt nach einer speziellen Art der Darstellung der zugrundeliegenden Informationen, damit sie optimal in den erforderlichen Entscheidungsschritt eingebunden werden können. Im Einzelnen werden dazu Aspekte der Aktivierung, der Beobachtung und Identifizierung von kritischen Zuständen, der regelbasierten Ableitung von Handlungsbedarf, der Bildung von Zustandsgrößen und der wissensbasierten Analyse und Handlungsplanung aufgegriffen und anhand eines Anwendungsbeispiels konkretisiert. Dazu wurde exemplarisch eine Mensch-Maschine-Schnittstelle konzipiert und prototypisch in einer Experimental- und Testumgebung realisiert, mit deren Hilfe Prozesse der Luftraumüberwachung und Lagebewertung durch einen Operateur auf verschiedenen kognitiven Ebenen unterstützt werden sollen.

Gegenstand dieses Beitrags ist die empirische Untersuchung eines webbasierten Buchungsportals für Flugtickets. Zunächst wird die Methodik der Blickbewegungsanalyse kurz vorgestellt, die als Evaluationsverfahren eingesetzt wurde. Damit stehen zusätzlich zu Leistungsdaten (wie Ausführungszeiten und Fehler) weitere verhaltensbeschreibende objektive Daten zur Verfügung, die durch in Benutzerbefragungen erhobene, subjektive software-ergonomische Bewertungen ergänzt werden. Ziel der Untersuchung war es, durch diese Methodenkombination Gebrauchstauglichkeitsdefizite der Buchungsoberfläche aufzudecken und daraus Gestaltungshinweise zur software-ergonomischen Verbesserung abzuleiten. Am Experiment nahmen 20 Probanden teil, die jeweils drei repräsentative Buchungsaufgaben lösen mussten. Die Analyse der Blickbewegungs- und Leistungsdaten sowie die Fragebogenauswertung identifizierten teilweise übereinstimmende Problembereiche, ein Teil der Defizite konnte jedoch nur anhand der Blickbewegungsdaten aufgedeckt werden.

Als Überwachungstätigkeit wird im Allgemeinen die leitende Kontrolle (Supervisory Control) meist komplexer Systeme oder Prozesse durch einen menschlichen Operateur verstanden. Diese Aufgabe des Operateurs lässt sich qualitativ als übergeordneter Regelungsvorgang beschreiben, bei dem der Zustand eines Prozesses oder Systems beobachtet wird, ein Vergleich mit dem Sollzustand hergestellt wird und ggf. eine Korrekturhandlung geplant und ausgeführt wird.

OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.

Simulation on human tasks has become increasingly important in manufacturing systems' design, assessment and improvement. In order to evaluate working processes and human factors in autonomous production cells (APCs), a collective analyzis of human-centred simulation approaches in advanced manufacturing systems is carried out, and a human task-oriented simulation methodology is proposed in this paper. Based on the methodology, a comparative simulation study is carried out in a 3D laser welding production cell from the aspects of human task spectrum, human error and occupational requirements. Human task network models of both conventional working process and APC working process are constructed using timed coloured Petri Nets. The evaluation criteria architecture for the simulation is formulated and the respective algorithms and parameters for the criteria are discussed in detail. A simulation program using C++ is developed based on Poses++ Petri Net simulator. The simulation results show some important transitions of system performance and human factors from the conventional production cell to the APC, which can support the decision-making when redesigning the conventional production cells to fit the requirements of APCs.

The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting “folksonomies??. An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure.

The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting “folksonomies�?. An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure.

Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification?? of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.

Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification�? of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.

This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.

1. Host plant finding in walking herbivorous beetles is still poorly understood. Analysis of small-scale movement patterns under semi-natural conditions can be a useful tool to detect behavioural responses towards host plant cues. 2. In this study, the small-scale movement behaviour of the monophagous leaf beetle Cassida canaliculata Laich. (Coleoptera: Chrysomelidae) was studied in a semi-natural arena (r = 1 m). In three different settings, a host (Salvia pratensis L., Lamiales: Lamiaceae), a non-host (Rumex conglomeratus Murr., Caryophyllales: Polygonaceae), or no plant was presented in the centre of the arena. 3. The beetles showed no differences in the absolute movement variables, straightness and mean walking speed, between the three settings. However, the relative movement variables, mean distance to the centre and mean angular deviation from walking straight to the centre, were significantly smaller when a host plant was offered. Likewise, the angular deviation from walking straight to the centre tended to decline with decreasing distance from the centre. Finally, significantly more beetles were found on the host than on the non-host at the end of all the trials. 4. It is concluded that C. canaliculata is able to recognise its host plant from a distance. Whether olfactory or visual cues (or a combination of both) are used to find the host plant remains to be elucidated by further studies.

The importance of olfactory versus contact cues for host plant recognition was investigated in the tortoise beetle Cassida canaliculata Laich. (Coleoptera: Chrysomelidae), which is strictly monophagous on meadow sage. The reaction of adult beetles to olfactory and contact host cues was tested using three bioassays (locomotion compensator, six-chamber-olfactometer, 'stem arena') to account for different behavioral contexts. Bioassay-guided fractionation of plant extracts was elaborated to characterize the nature of contact stimuli. The beetles were only slightly attracted to odors from small amounts of leaf material. However, when contact cues were provided additionally, the beetles showed strong preferences for samples of their host plant over controls. Bioassay-guided fractionation led to isolation of at least two non-polar contact stimuli acting in concert that are sufficient for host plant identification in C. canaliculata.

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we present two tag recommendation algorithms: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, an adaptation of the well-known PageRank algorithm that can cope with undirected triadic hyperedges. We evaluate and compare both algorithms on large-scale real life datasets and show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

We present a user profile management component for ubiquitous computing environments that provides management, inquiry and delivery of context-dependent user preferences. In particular, our approach takes into account that users act differently depending on their current context, and hence, that a user can be described by different submodels that represent the user's preferences within different contexts. Furthermore, our approach takes into account that different applications usually adhere to different semantics

Bookmarks (or Favorites, Hotlists) are a popular strategy to relocate interesting websites on the WWW by creating a personalized local URL repository. Most current browsers offer a facility to store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable taxonomy. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification?? of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbour-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. Additionally, a procedure to generate keyword recommendations is proposed to ease the annotation of new bookmarks. A prototype system called CariBo has been implemented as a plugin of the central bookmark server software SiteBar. A case study conducted with real user data supports the validity of the approach.

Bookmarks (or Favorites, Hotlists) are a popular strategy to relocate interesting websites on the WWW by creating a personalized local URL repository. Most current browsers offer a facility to store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable taxonomy. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification�? of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbour-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. Additionally, a procedure to generate keyword recommendations is proposed to ease the annotation of new bookmarks. A prototype system called CariBo has been implemented as a plugin of the central bookmark server software SiteBar. A case study conducted with real user data supports the validity of the approach.

Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -

Egg distribution in herbivorous beetles can be affected by bottom-up (host plant), and by top-down factors (parasitoids and predators), as well as by other habitat parameters. The importance of bottom-up and top-down effects may change with spatial scale. In this study, we investigated the influence of host plant factors and habitat structure on egg distribution in the leaf beetle Cassida canaliculata Laich. (Coleoptera: Chrysomelidae), a monophagous herbivore on Salvia pratensis L. (Lamiales: Lamiaceae), on four spatial scales: individual host plant, microhabitat, macrohabitat, and landscape. At the individual host plant scale we studied the correlation between egg clutch incidence and plant size and quality. On all other scales we analyzed the relationship between the egg clutch incidence of C. canaliculata and host plant percentage cover, host plant density, and the surrounding vegetation structure. Vegetation structure was examined as herbivores might escape egg parasitism by depositing their eggs on sites with vegetation factors unfavorable for host searching parasitoids. The probability that egg clutches of C. canaliculata were present increased with an increasing size, percentage cover, and density of the host plant on three of the four spatial scales: individual host plant, microhabitat, and macrohabitat. There was no correlation between vegetation structure and egg clutch occurrence or parasitism on any spatial scale. A high percentage of egg clutches (38–56%) was parasitized by Foersterella reptans Nees (Hymenoptera: Tetracampidae), the only egg parasitoid, but there was no relationship between egg parasitism and the spatial distribution of egg clutches of C. canaliculata on any of the spatial scales investigated. However, we also discuss results from a further study, which revealed top-down effects on the larval stage.

Herbivorous insects are the major link between primary producers and a multitude of animals at higher trophic levels. Elucidating the causes and consequences of their distribution patterns in the "green world" is thus essential for our understanding of numerous ecological processes on multiple spatial scales. We can ask where and why a certain herbivore can be found in the landscape, within the habitat, on which plant within the habitat and finally, where on that plant. Depending on spatial scale the distribution of herbivores is shaped by different processes (fitness considerations, physiological abilities, population dynamics, dispersal behavior, history of the landscape etc.). Scaling down from fragmented landscapes to individual host plants this thesis analyzes the distribution patterns of the strictly monophagous herbivore Cassida canaliculata Laich. (Coleoptera: Chrysomelidae), which feeds and oviposits exclusively on meadow sage, Salvia pratensis L. (Lamiales: Lamiaceae), and compares it to those of the polyphagous tansy leaf beetle Galeruca tanaceti L. (Coleoptera: Chrysomelidae), which does not oviposit on its host plants, but on dry non-host structures. The specialist Cassida canaliculata depended on all spatial scales (fragmented landscape, microhabitat and host plant individual) mainly on the distribution and quality of its single host plant species Salvia pratensis, whereas enemy-free-space - i.e. avoidance of parasitism and predation of egg clutches, larvae, and pupae - seemed to influence oviposition site choice only on the scale of the host plant individual. On this spatial scale, offspring of Cassida canaliculata had a higher chance of survival on large host plant individuals, which were also preferred for oviposition by the females. In contrast, the distribution patterns of the generalist Galeruca tanaceti was shaped by the interaction with its parasitoid regarding both microhabitat choice and egg distribution within individual host plants. On the microhabitat scale, beetles could escape from their parasitoids by ovipositing into high and dense vegetation. Regarding oviposition site choice within a host plant individual, females oviposited as high as possible in the vegetation and could thus reduce both the risk of parasitism and the probability of winter mortality. The results of my thesis show that the degree of specificity of a herbivore is of central importance for the resulting egg distribution pattern on all spatial scales.

A key argument for modeling knowledge in ontologies is the easy reuse and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA).While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references in a kind of personal library.

Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies, briefly describeour own system BibSonomy, which allows for sharing both bookmarks andpublication references, and discuss first steps towards emergent semantics.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies,called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to findcommunities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.

As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.

As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents.One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particula

As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents. One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.

Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "Web 2.0" -- stellen Folksonomies dar. In diesen Systemen koennen Benutzer gemeinsam Ressourcen verwalten undmit Schlagwoertern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansaetze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.

In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.

Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "`Web 2.0"' -- stellen Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.

Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "Web 2.0" -- stellen Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.

Oviposition site selection in insects is essential in terms of low egg mortality, high offspring survival and therefore a high reproductive output. Although oviposition height could be a crucial factor for the fitness of overwintering eggs, it has rarely been investigated. In this study the oviposition height of a polyphagous leaf beetle, Galeruca tanaceti Linnaeus in different habitats and at different times of the season was examined and its effect on egg clutch mortality was recorded. The leaf beetle occurs as an occasional pest on several agricultural plants. It deposits its eggs within herbaceous vegetation in autumn. Eggs are exposed to numerous biotic and abiotic mortality factors summarized as egg parasitism and winter mortality. Oviposition height of the leaf beetle was not uniform, but changed significantly with the structure of the habitat and during the season. Mean oviposition height per site (70.2±4.9 cm) was significantly higher than mean vegetation height (28.4±2.4 cm). Height of plants with egg clutches attached and oviposition height were significantly positively correlated. The results suggest that females try to oviposit as high as possible in the vegetation and on the plants selected. In accordance with this, the probability of egg parasitism and of winter egg clutch mortality significantly declined with increasing oviposition height. A preference of G. tanaceti for oviposition sites high up in the vegetation might therefore have evolved due to selection pressures by parasitoids and winter mortality.

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded.This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Wissensmanagement in zentralisierten Wissensbasen erforderteinen hohen Aufwand fuer Erstellung und Wartung, und es entspricht nichtimmer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen UEberblickueber zwei aktuelle Ansaetze, die durch kollaboratives Wissensmanagementdiese Probleme loesen koennen. Im Peer-to-Peer-Wissensmanagement unterhaltenBenutzer dezentrale Wissensbasen, die dann vernetzt werden koennen, umandere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, dieWissensakquisition so einfach wie moeglich zu gestalten und so viele Benutzer inden Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.