Social Bookmarking am Beispiel BibSonomy.
In:
A. Blumauer and T. Pellegrini, editors,
Social Semantic Web, chapter 18, pages 363-391.
Springer, Berlin, Heidelberg, 2009.
Andreas Hotho, Robert Jäschke, Dominik Benz, Miranda Grahl, Beate Krause, Christoph Schmitz and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Discovering Shared Conceptualizations in Folksonomies.
Web Semantics: Science, Services and Agents on the World Wide Web, 6(1):38-53, 2008.
Robert Jäschke, Andreas Hotho, Christoph Schmitz, Bernhard Ganter and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Logsonomy - A Search Engine Folksonomy.
In:
Proceedings of the Second International Conference on Weblogs and Social Media (ICWSM 2008), pages 192-193.
AAAI Press, Menlo Park, CA, USA, 2008.
Robert Jäschke, Beate Krause, Andreas Hotho and Gerd Stumme.
[doi]
[abstract]
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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.
Logsonomy - Social Information Retrieval with Logdata.
In:
HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, pages 157-166.
ACM, New York, NY, USA, 2008.
Beate Krause, Robert Jäschke, Andreas Hotho and Gerd Stumme.
[doi]
[abstract]
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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.
Tag Recommendations in Folksonomies.
In: A. Hinneburg, editor,
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), pages 13-20.
Martin-Luther-Universität Halle-Wittenberg, 2007.
Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme and Gerd Stumme.
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[abstract]
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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.
Semantic Network Analysis of Ontologies.
In:
Y. Sure and J. Domingue, editors,
The Semantic Web: Research and Applications, pages 514-529.
Springer, Berlin/Heidelberg, 2006.
10.1007/11762256_38
Bettina Hoser, Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
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[abstract]
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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.
BibSonomy: A Social Bookmark and Publication Sharing System.
In: A. de Moor, S. Polovina and H. Delugach, editors,
Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures.
Aalborg University Press, Aalborg, Denmark, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[doi]
[BibTeX]
Emergent Semantics in BibSonomy.
In: C. Hochberger and R. Liskowsky, editors,
Informatik 2006 - Informatik für Menschen, volume 94, series Lecture Notes in Informatics, pages 305-312.
Gesellschaft für Informatik, Bonn, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Trend Detection in Folksonomies.
In: Y. S. Avrithis, Y. Kompatsiaris, S. Staab and N. E. O'Connor, editors,
Proc. First International Conference on Semantics And Digital Media Technology (SAMT) , volume 4306, series Lecture Notes in Computer Science, pages 56-70.
Springer, Heidelberg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Content Aggregation on Knowledge Bases using Graph Clustering.
In: Y. Sure and J. Domingue, editors,
The Semantic Web: Research and Applications, volume 4011, series Lecture Notes in Computer Science, pages 530-544.
Springer, Berlin/Heidelberg, 2006.
Christoph Schmitz, Andreas Hotho, Robert Jäschke and Gerd Stumme.
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[abstract]
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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.