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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - de Moor, Aldo A2 - Polovina, Simon A2 - Delugach, Harry T1 - BibSonomy: A Social Bookmark and Publication Sharing System T2 - Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures PB - Aalborg Universitetsforlag C1 - Aalborg PY - 2006/ CY - VL - IS - SP - 87 EP - 102 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf DO - KW - 2006 KW - FCA KW - OntologyHandbook KW - bibsonomy KW - bookmarking KW - folksonomy KW - iccs KW - l3s KW - myown KW - nepomuk KW - social KW - tagorapub L1 - SN - 87-7307-769-0 N1 - Publications of Gerd Stumme N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Hochberger, Christian A2 - Liskowsky, Rüdiger T1 - Emergent Semantics in BibSonomy T2 - Informatik 2006 -- Informatik für Menschen. Band 2 PB - Gesellschaft für Informatik C1 - Bonn PY - 2006/october CY - VL - P-94 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf DO - KW - 2006 KW - UniK KW - bibsonomy KW - emergence KW - emergent KW - folksonomy KW - hotho KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - schmitz KW - semantics KW - stumme KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - T1 - FolkRank: A Ranking Algorithm for Folksonomies T2 - Proc. FGIR 2006 PB - C1 - PY - 2006/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006folkrank.pdf DO - KW - 2006 KW - algorithm KW - folkrank KW - ir KW - itegpub KW - l3s KW - myown KW - nepomuk KW - pagerank KW - ranking L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Avrithis, Yannis S. A2 - Kompatsiaris, Yiannis A2 - Staab, Steffen A2 - O'Connor, Noel E. T1 - Trend Detection in Folksonomies T2 - Proc. First International Conference on Semantics And Digital Media Technology (SAMT) PB - Springer C1 - Heidelberg PY - 2006/12 CY - VL - 4306 IS - SP - 56 EP - 70 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf DO - KW - 2006 KW - UniK KW - detection KW - folkrank KW - folksonomy KW - hotho KW - intranet KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - pagerank KW - schmitz KW - stumme KW - tagorapub KW - trend KW - triadic L1 - SN - 3-540-49335-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Braß, Stefan A2 - Hinneburg, Alexander T1 - Wege zur Entdeckung von Communities in Folksonomies T2 - Proc. 18. Workshop Grundlagen von Datenbanken PB - Martin-Luther-Universität C1 - Halle-Wittenberg PY - 2006/06 CY - VL - IS - SP - 80 EP - 84 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006wege.pdf DO - KW - 2006 KW - bibsonomy KW - communities KW - community KW - detection KW - itegpub KW - l3s KW - myown KW - nepomuk KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Content Aggregation on Knowledge Bases using Graph Clustering T2 - The Semantic Web: Research and Applications PB - Springer C1 - Heidelberg PY - 2006/ CY - VL - 4011 IS - SP - 530 EP - 544 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf DO - KW - 2006 KW - aggregation KW - clustering KW - content KW - graph KW - itegpub KW - l3s KW - myown KW - nepomuk KW - ontologies KW - ontology KW - seminar2006 KW - theory L1 - SN - N1 - N1 - AB - 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. ER - TY - GEN AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Pellegrini, Tassilo A2 - Blumauer, Andreas T1 - Kollaboratives Wissensmanagement JO - PB - Springer C1 - PY - 2006/ VL - IS - SP - 273 EP - 290 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf DO - KW - 2006 KW - Wissensmanagement KW - collaborative KW - folksonomy KW - knowledge KW - l3s KW - management KW - myown KW - nepomuk KW - p2p KW - semantic KW - web KW - wissensmanagement L1 - N1 - N1 - AB - 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. ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - Žiberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification. Proceedings of the 10th IFCS Conf. PB - Springer C1 - Heidelberg PY - 2006/07 CY - VL - IS - SP - 261 EP - 270 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf DO - KW - 2006 KW - analysis KW - fca KW - folksonomies KW - folksonomy KW - l3s KW - myown KW - nepomuk KW - network KW - semantic L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - 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. ER - TY - JOUR AU - Stumme, Gerd AU - Hotho, Andreas AU - Berendt, Bettina T1 - Semantic Web Mining - State of the Art and Future Directions JO - Journal of Web Semantics PY - 2006/ VL - 4 IS - 2 SP - 124 EP - 143 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/stumme2006semantic.pdf DO - KW - 2006 KW - l3s KW - mining KW - myown KW - semantic KW - sota KW - survey KW - web L1 - SN - N1 - N1 - AB - SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
improving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
Web itself.
The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
these resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
formalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
integration could be profitable. ER -