Jäschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J. N.; Koronacki, J.; de Mántaras, R. L.; Matwin, S.; Mladenic, D. & Skowron, A. (Hrsg.):
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin, Heidelberg: Springer, 2007 (Lecture Notes in Computer Science 4702), S. 506-514
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hoser, B.; Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Semantic Network Analysis of Ontologies. In: Sure, Y. & Domingue, J. (Hrsg.):
The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 514-529
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. In: de Moor, A.; Polovina, S. & Delugach, H. (Hrsg.):
Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures. Aalborg: Aalborg Universitetsforlag, 2006, S. 87-102
[Volltext] [Kurzfassung]
[BibTeX]
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 and briefly describe our own system BibSonomy, which allows for sharing both bookmarksand publication references in a kind of personal library.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. In: de Moor, A.; Polovina, S. & Delugach, H. (Hrsg.):
Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures. Aalborg: Aalborg Universitetsforlag, 2006, S. 87-102
[Volltext] [Kurzfassung]
[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 and briefly describe
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Das Entstehen von Semantik in BibSonomy.
Social Software in der Wertschöpfung. Baden-Baden: Nomos, 2006
[Volltext] [Kurzfassung]
[BibTeX]
Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Emergent Semantics in BibSonomy. In: Hochberger, C. & Liskowsky, R. (Hrsg.):
Informatik 2006 - Informatik für Menschen. Band 2. Bonn: Gesellschaft für Informatik, 2006 (Lecture Notes in Informatics P-94)
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Emergent Semantics in BibSonomy. In: Hochberger, C. & Liskowsky, R. (Hrsg.):
Informatik 2006 - Informatik für Menschen. Band 2. Bonn: Gesellschaft für Informatik, 2006 (Lecture Notes in Informatics P-94)
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: FolkRank: A Ranking Algorithm for Folksonomies.
Proc. FGIR 2006. 2006
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Trend Detection in Folksonomies. In: Avrithis, Y. S.; Kompatsiaris, Y.; Staab, S. & O'Connor, N. E. (Hrsg.):
Proc. First International Conference on Semantics And Digital Media Technology (SAMT) . Heidelberg: Springer, 2006 (LNCS 4306), S. 56-70
[Volltext] [Kurzfassung]
[BibTeX]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Trend Detection in Folksonomies. In: Avrithis, Y. S.; Kompatsiaris, Y.; Staab, S. & O'Connor, N. E. (Hrsg.):
Proc. First International Conference on Semantics And Digital Media Technology (SAMT) . Heidelberg: Springer, 2006 (LNCS 4306), S. 56-70
[Volltext] [Kurzfassung]
[BibTeX]
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.
Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.: TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06). Hong Kong: IEEE Computer Society, 2006, S. 907-911
[Volltext]
[BibTeX]
Jäschke, R.; Hotho, A.; Schmitz, C. & Stumme, G.: Wege zur Entdeckung von Communities in Folksonomies. In: Braß, S. & Hinneburg, A. (Hrsg.):
Proc. 18. Workshop Grundlagen von Datenbanken. Halle-Wittenberg: Martin-Luther-Universität , 2006, S. 80-84
[Volltext] [Kurzfassung]
[BibTeX]
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.
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Content Aggregation on Knowledge Bases using Graph Clustering. In: Sure, Y. & Domingue, J. (Hrsg.):
The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 530-544
[Volltext] [Kurzfassung]
[BibTeX]
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.
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Mining Association Rules in Folksonomies. In: Batagelj, V.; Bock, H.-H.; Ferligoj, A. & �iberna, A. (Hrsg.):
Data Science and Classification. Proceedings of the 10th IFCS Conf.. Heidelberg: Springer, 2006Studies in Classification, Data Analysis, and Knowledge Organization , S. 261-270
[Kurzfassung]
[BibTeX]
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.
Schmitz, C.; Hotho, A.; J�schke, R. & Stumme, G.: Mining Association Rules in Folksonomies. In: Batagelj, V.; Bock, H.-H.; Ferligoj, A. & �iberna, A. (Hrsg.):
Data Science and Classification. Proceedings of the 10th IFCS Conf.. Heidelberg: Springer, 2006Studies in Classification, Data Analysis, and Knowledge Organization , S. 261-270
[Kurzfassung]
[BibTeX]
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.
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Mining Association Rules in Folksonomies. In: Batagelj, V.; Bock, H.-H.; Ferligoj, A. & Žiberna, A. (Hrsg.):
Data Science and Classification. Proceedings of the 10th IFCS Conf.. Heidelberg: Springer, 2006Studies in Classification, Data Analysis, and Knowledge Organization , S. 261-270
[Volltext] [Kurzfassung]
[BibTeX]
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.
Proceedings of the First Workshop on Semantic Network Analysis . Aachen, 2005
[Volltext]
[BibTeX]
Proceedings of the First Workshop on Semantic Network Analysis . Aachen, 2005
[Volltext]
[BibTeX]
Cole, R. J.; Eklund, P. W. & Stumme, G.: Document Retrieval for Email Search and Discovery using Formal Concept Analysis. In:
Journal of Applied Artificial Intelligence (AAI) 17 (2003), Nr. 3, S. 257-280
[Volltext]
[Kurzfassung]
[BibTeX]
This paper discusses an document discovery tool based on
conceptual clustering by formal concept analysis. The program
allows users to navigate email using a visual lattice metaphor
rather than a tree. It implements a virtual file structure over
email where files and entire directories can appear in multiple
positions. The content and shape of the lattice formed by the
conceptual ontology can assist in email discovery. The system
described provides more flexibility in retrieving stored emails
than what is normally available in email clients. The paper
discusses how conceptual ontologies can leverage traditional
document retrieval systems and aid knowledge discovery in document
collections.