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.

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.

The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (e. g., persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on fact retrieval or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information. Our semantic search engine, built on semantic teleporting and spreading activation, is able to answer natural language queries with facts, e. g., a specific phone number, and/or relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.

Presentation of case studies of successfull Semantic Web use, NEPOMUK is one of them. Audience are Oil Industry and Defence Industry IT Executives.

KDE version 4.0 is a significant release that has been available since January 11, 2008. The libraries, desktop, applications, and artwork have been improved and adapted, and it now runs natively on a wider set of operating systems that include Linux, BSD, Solaris, Windows and Mac OS X. As part of this release, the search engine was overhauled and desktop annotation introduced. Based on standards and technologies developed in the NEPOMUK Semantic Desktop project, the search engine is now based on RDF and allows users to annotate and search for their files using Semantic Web standards.

Die formale Modellierung von Fachdomänen auf Basis von Webstandards wurde ursprünglich von einer Community um Tim Berners-Lee (W3C) vorangetrieben. Es werden Beispiele vorgestellt sowie Startpunkte für die Suche nach Ontologien gegeben, die sich für EAI via SOA und Web anbieten. Im Anschluss werden Tools gezeigt, die die einschlägigen Standards (RDF, OWL, SPARQL) auf Java-Ebene transportieren.

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.

With the growth of the Semantic Web, the requirements on storing and querying RDF has become more sophisticated. When a larger amount of data has to be managed, queries in structured query languages, such as SPARQL, are not always powerful enough. Use of additional keywords for querying can further reduce the result set towards the actual relevant answers, however, SPARQL only provides complete string matching or filtering based on regular expressions, which is a very slow operation. In contrast, state of the art Information Retrieval (IR) techniques provide sophisticated features such as keyword search, lemmatisation, stemming and ranking. In this paper we present a combination of structured RDF queries and full-text search. It is implemented as an extension of an established RDF store (Sesame) with IR capabilities using the text search library Lucene, without requiring modifications to existing RDF query languages.

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.

Der Semantic Desktop macht den PC zum Denkwerkzeug. Wir haben genug Platz, um all unsere E-Mails, MP3s, Photos, Videos und Dokumente am Desktop zu speichern. Das Problem ist, diese Information zu verwalten. Dateisysteme bieten nur starre Hierarchien an. Tim Berners-Lee und das W3C haben bereits weiter gedacht: Menschen denken in Konzepten, das Semantic Web bietet mit RDF und Ontologien einen auf HTTP, URIs und HTML aufbauenden Standard zur Annotation und Suche. Der Semantic Desktop bringt Betriebssysteme und Anwendungen damit weg von den Dateien, auf die Stufe der Gedanken.

This paper introduces the NEPOMUK project which aims to create a standard and reference implementation for the Social Semantic Desktop. We outline the requirements and functionalities that were identified for a useful Semantic Desktop system and present an architecture that fulfills these requirements which was acquired by incremental refinement of the architecture of existing Semantic Desktop prototypes. The NEPOMUK project is primarily motivated by three real-life industrial use-cases, we briefly outline these and the processes used to extract required functionalities from the people working in these areas today, and we present a selection of typical tasks where the Semantic Desktop could be of benefit.

This paper examines issues on Philosophy of Artificial Intelligence (AI), Cognitive Science and Mental Models. The paper provides a philosophical grounding for the researchers in Personal Information Management (PIM). An overview is given on various philosophical aspects of computer-based activities. Discussions on the theories relevant to understand the goals for the Semantic Desktop community are elicited. Philosophical theories are not immediately transparent to the programmers, but the ideas discussed here are intended to emphasize a theoretical foundation, with respect to Semantic Desktop long term goals. The goal of this paper is to examine the theories of Philosophy and to provide a conceptual idea to design user-intuitive Semantic Desktops. The paper tries to induce scientific curiosity among the Semantic Desktop researchers as well as to develop the future Semantic Desktops to realize Weak AI.

ConTag is an approach to generate semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology. Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web 2.0 services.

At last year's Wikimania, we presented the idea of combining Wikipedia, the world's biggest collaboratively created source of encyclopedic knowledge, and the Semantic Web, a project to create a world wide decentralized knowledge base on top of the current web. We identified the need to allow knowledge processing in a computer assisted way within Wikipedia, for example to intelligently query the knowledge base, and allow for new navigation, browsing and knowledge management features, like automatically created lists or unit conversion. To this end, we proposed the introduction of typed links as a simple and unintrusive way for rendering large parts of Wikipedia machine readable. Since last year the proposal was discussed, changed and extended. Implementation has started, and the extension has been installed and tested in several sites. In this paper we present the extensions that have been included to the original proposal and the current state of the implementation, as well as some of the future plans.

Wikipedia is the world's largest collaboratively edited source of encyclopaedic knowledge. But in spite of its utility, its content is barely machine-interpretable and only weakly structured. With Semantic MediaWiki we provide an extension that enables wiki-users to semantically annotate wiki pages, based on which the wiki contents can be browsed, searched, and reused in novel ways. In this paper, we give an extended overview of Semantic MediaWiki and discuss experiences regarding performance and current applications.

Knowledge articulation costs are the bottleneck for efficient Personal Knowledge Management (PKM). Current tools either allow to few structures and hence have to rely only on keyword searches in plain text, allow no associative browsing, and cannot infer new knowledge. Semantic modelling tools on the other hands are too cumbersome to use and force the user to formalise everything all the time ? this is too costly in PKM usage. Conceptual Data Structures (CDS) are what is found to be the largest common denominator of information structures used in common knowledge artefacts. CDS allow step-wise and gradual formalisation and representing the spectrum from informal notes up to formal ontologies. This paper describes the CDS data model and ontology in detail and shows how CDS can largely be implemented with existing semantic web technologies.

There is currently no model that is capable of representing the actual content of web resources together with their semantic web meta-data. This paper presents requirements for semantic content management, a unified human-browsable and human-editable semantic web content model (SWCM), and its implementation swecr.

This paper considers two paradigms of process-aware information systems (PAIS) that are used to share knowledge about planning and executing tasks. The case-based task management (CBTM) system is based on the transduction of execution protocols, so-called cases; and the pattern-based task management (PBTM) system is based on task patterns that are derived from cases but yield more abstract information. As user motivation to contribute to a collaborative task management system is a crucial factor for its success, we consider the different motivational requirements of CBTM and PBTM from a psychological point of view. Based on experimental findings about different factors that have been shown to enhance people's motivation to contribute their knowledge to a shared pool, we compare the two approaches and assess which one is more appropriate with respect to motivation.

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.

The TFxIDF term weighting scheme is the standard approach on vectorization of textual data. For a data set where textual data stemming from web document structure is to be vectorized \cite{DBLP:conf/kdxd/BrunzelS06} the need for a enhanced term weighting scheme arose. In this publication we introduce a term weighting scheme which improves the behavior compared to the traditional TFxIDF scheme by adding a component which is based on the linguistically inspired notion of domain relevance. Domain relevance measures the degree to which a term is regarded as more relevant within a data set compared to a reference data set. By means of this external component a potential weakness of TFxIDF on non standard distributed data sets is overcome. This weighting scheme favours domain relevant terms, which can be regarded as more useful in settings where the clustering is performed to be consumed by an human supervisor e.g for semi-automatic ontology learning.

Due to their different provenience, a thorough integration between Knowledge Management Systems (KMS) and Knowledge Work Support, e.g., Task Management Systems, is still missing. In the present paper we identify several points of fracture in the information transfer process between both. Hereupon we argue that the recently suggested Pattern-Based Task Management (PBTM) better integrates the two paradigms, leading to an entanglement between both sides. It results a knowledge lifecycle that goes beyond single-loop learning. By the introduction of task patterns that guide users in work planning and execution and motivate collective improvement, PBTM even fosters double-loop learning.

Knowledge work often suffers from the inexpertness of novices in an organization. Therefore, one important goal within knowledge work is to turn novices into experts. For gaining expertise an important factor is implicit knowledge (IK). Even if Information Technology cannot transfer IK directly, it can support the transfer more or less efficiently. We present a knowledge management system, the pattern-based task management (PBTM), which addresses this difficult challenge. Invoking the model of basic communication [Wal05], we show how PBTM aids the experienced user to better provide contextualized knowledge and the inexperienced user to better internalize it again in order to gain expertise.

Collective knowledge construction is often inhibited by motivational barriers. In this paper we present pattern-based task management (PBTM) as a system to construct collective knowledge and show ways how to foster motivation. We present the motivational structure of the knowledge sharing situation as a social dilemma which feeds free-riding. To avoid this, we concentrate on ways to overcome the dilemma from a psychological perspective by presenting concrete suggestions on increasing users' motivation to actively participate in PBTM. The goal is to foster collective knowledge con-struction as a social process.

In this paper, we propose the use of Semantic Web technologies to bridge the gap between authoring systems and authors. The core part of our solution is the ontology-based framework that captures the information about the interaction between learning objects (LOs) and four main roles in the educational process (content author, instructional designer, teacher, and learner), and then according to this information proposes the most relevant learning content to the author. The central part of the framework is the Request-Recommendation ontology that formalizes the author?s request along with a set of learning content proposals (recommendation), as a response to that request. Furthermore, we propose the use of a weighting scheme to calculate the weight of the content proposals and thus enable their ranking within the recommendation.

This paper presents an ontology-based framework for capturing information about interaction between authors and learning contents. The approach is primarily focused on keeping track of changes made to a learning content as well as information about authors and tools, which take part in the interaction. We introduce the Author-Learning Content Interaction (ALCI) ontology, which gives a formal representation of changes and describes possible aspects of the interaction. Moreover, we discuss how information collected with the framework can assist authors during the authoring process and how it can support evolution and versioning of a learning content.

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.

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 such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.

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.

Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.

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.

Held a workshop on "The Semantic Desktop", presented a paper on "Semantic Desktop 2.0: The Gnowsis Experience", presented a poster and demo on the same topic. Intensified connections to various industrial and academic partners of Nepomuk.

iMapping is a technique for visually structuring information objects. It supports the full range from informal note taking over semi-structured personal information management to formal knowledge models. With iMaps, users can easily go from overview to fine-grained structures while browsing, editing or refining the knowledge base in one comprehensive view. An iMap is comparable to a large white-board where information items can be positioned like post-its but also nested into each other. Spatial browsing and zooming as well as graphical editing facilities make it easy to structure content in an intuitive way. iMapping builds on a zooming user interface approach to facilitate navigation and to help users maintain an overview in the knowledge space. While a first implementation is being developed, iMapping is still in a conceptual stage. In this paper we describe the iMapping approach and how it tries to combine and extend the advantages of other approaches.

The Semantic Desktop brings the ideas and the technologies of the Semantic Web into the personal computer desktop. As a prerequisite for applying Semantic Web technologies to a certian domain of knowledge an ontological model of the domain is required. In the Gnowsis Semantic Desktop, the PIMO (Personal Information Model Ontology) addresses this problem by providing a generic lightweight ontology whose classes model the mian concepts involved in the daily activities of a person: places, organizations, persons, etc. But in order to be fully useful for a certain user, this generic model needs to be personalized and populated, adding more classes and concrete instances of the existent classes. As the process of manual population could be tedious and time consuming, in this paper we propose an alternative which tries to exploit the information that the user provides while performing Web searches. Apart from populating the PIMO, our approach is useful in resource annotation.

The Semantic Desktop needs a well-thought use of ontologies and ontology languages. Existing ontology languages like RDF/S, OWL, SKOS and Topic Maps are very well suited for certain application areas, but do not fulfill some of the requirements given on the Semantic Desktop. In this report, a new ontology language, extending RDF/S, is proposed, the PIMO ontology language, which addresses the requirements of the Semantic Desktop and uses existing solutions as an inspiration to build a suitable solution. The language contains a core upper ontology, defining basic classes for things, concepts, resources, persons, etc. and also stops at these basic entities. Extending the ontology definitions of classes and relations is possible by PIMO-domain ontologies. The core application area of the PIMO-language is to allow individual persons to express their own mental models in a structured way, the different mental models can then be integrated based on matching algorithms or on domain ontologies. Based on the core upper ontology elements, each user can extend his personal mental model in an open manner. Accompanying to the description of the ontology is a RDF/S version of the ontology language, created using the popular Protégé tool. An example of the mental model of a user is given, the fictional user "Paul" is further described. With the gnowsis-beta open source software, an implementation based on the PIMO language exists, that allows validating ontology files. A web-service for that will be provided soon. The ontology language, this document, the open-source reference implementation and the example documents can provide a stable basis for discussions on this topic and allow you to extend your own work.

In this paper we present lessons learned from building a Semantic Desktop system, the gnowsis beta. On desktop computers, semantic software has to provide stable services and has to reflect the personal view of the user. Our approach to ontologies, the Personal Information Model PIMO allows to create tagging services like del.icio.us on the desktop. A semantic wiki allows further annotations. Continuous evaluations of the system helped to improve it. These results were created in the EPOS research project and are available in the open source projects Aperture, kaukoluwiki, and gnowsis and will be continued in the Nepomuk project. By using these components, other developers can create new desktop applications the web 2.0 way.