Ackermann, M.; Berendt, B.; Grobelnik, M.; Hotho, A.; Mladenic, D.; Semeraro, G.; Spiliopoulou, M.; Stumme, G.; Svatek, V. & van Someren, M. (Hrsg.): Semantics, Web and Mining. Heidelberg: Springer, 2006
[Volltext]

Proceedings of the 2nd Workshop on Semantic Network Analysis. , 2006
[Volltext]

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]
A key argument for modeling knowledge in ontologies is the easy
-use and re-engineering of the knowledge. However, beside
nsistency checking, current ontology engineering tools provide
ly basic functionalities for analyzing ontologies. Since
tologies can be considered as (labeled, directed) graphs, graph
alysis techniques are a suitable answer for this need. Graph
alysis has been performed by sociologists for over 60 years, and
sulted in the vivid research area of Social Network Analysis
NA). While social network structures in general currently receive
gh attention in the Semantic Web community, there are only very
w SNA applications up to now, and virtually none for analyzing the
ructure of ontologies.

e illustrate in this paper the benefits of applying SNA to
tologies and the Semantic Web, and discuss which research topics
ise on the edge between the two areas. In particular, we discuss
w different notions of centrality describe the core content and
ructure of an ontology. From the rather simple notion of degree
ntrality over betweenness centrality to the more complex
genvector centrality based on Hermitian matrices, we illustrate
e insights these measures provide on two ontologies, which are
fferent 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]
Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. The reason for their immediate success is the
ct that no specific skills are needed for participating. In this
per we specify a formal model for folksonomies and briefly describe
r own system BibSonomy, which allows for sharing both bookmarks
d publication references in a kind of personal library.

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]
Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. The reason for their immediate success is the
ct that no specific skills are needed for participating. In this
per we specify a formal model for folksonomies, briefly describe
r own system BibSonomy,
ich allows for sharing both bookmarks and
blication references,
d 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]
In social bookmark tools users are setting up
ghtweight conceptual structures called folksonomies. Currently,
e information retrieval support is limited. We present a formal
del and a new search algorithm for folksonomies, called
lkRank, that exploits the structure of the folksonomy. The
oposed algorithm is also applied to find communities within the
lksonomy and is used to structure search results. All findings are
monstrated on a large scale dataset. A long version of this paper
s been published at the European Semantic Web Conference
06.

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]
As the number of resources on the web exceeds by far the number of
cuments one can track, it becomes increasingly difficult to remain
to date on ones own areas of interest. The problem becomes more
vere with the increasing fraction of multimedia data, from which
is difficult to extract some conceptual description of their
ntents.

ne way to overcome this problem are social bookmark tools, which
e rapidly emerging on the web. In such systems, users are setting
lightweight conceptual structures called folksonomies, and
ercome thus the knowledge acquisition bottleneck. As more and more
ople participate in the effort, the use of a common vocabulary
comes more and more stable. We present an approach for discovering
pic-specific trends within folksonomies. It is based on a
fferential adaptation of the PageRank algorithm to the triadic
pergraph structure of a folksonomy. The approach allows for any
nd of data, as it does not rely on the internal structure of the
cuments. In particular, this allows to consider different data
pes in the same analysis step. We run experiments on a large-scale
al-world snapshot of a social bookmarking system.

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]
Ein wichtiger Baustein des neu entdeckten World Wide Web - des "`Web 2.0"' - stellen
lksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und
t Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen
n interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur
tdeckung 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]
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.: Kollaboratives Wissensmanagement. , 2006
[Volltext]
Wissensmanagement in zentralisierten Wissensbasen erfordert
nen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
mer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
er zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
ese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
nutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
dere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
ssensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
n Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.

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]
Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. These systems provide currently relatively few
ructure. We discuss in this paper, how association rule mining
n be adopted to analyze and structure folksonomies, and how the results can be used
r ontology learning and supporting emergent semantics. We
monstrate our approach on a large scale dataset stemming from an
line system.

Stumme, G.; Hotho, A. & Berendt, B.: Semantic Web Mining - State of the Art and Future Directions. In: Journal of Web Semantics 4 (2006), Nr. 2, S. 124-143
[Volltext]
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
b itself.
e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
tegration could be profitable.