Ackermann,Markus
Berendt,Bettina
Grobelnik,Marko
Hotho,Andreas
Mladenic,Dunja
Semeraro,Giovanni
Spiliopoulou,Myra
Stumme,Gerd
Svatek,Vojtech
vanSomeren,Maarten
Semantics, Web and Mining
2006
Workshop on Web Mining 2006 (WebMine)
2006
Bloehdorn,S.
Buntine,W.
Hotho,A.
Introduction to the Special Issue 'Learning in Web Search'
Informatica
30
141-141
2006
Bloehdorn,Stephan
Hotho,Andreas
Boosting for Text Classification with Semantic Features
Springer
3932
149–166
2006
Bloehdorn,Stephan
Cimiano,Philipp
Hotho,Andreas
Learning Ontologies to Improve Text Clustering and Classification
Springer Berlin Heidelberg
334–341
2006
Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.
ER -
Dellschaft,Klaas
Staab,Steffen
On How to Perform a Gold Standard based Evaluation of Ontology Learning
Springer, LNCS
2006
Haase,Peter
Ehrig,Marc
Hotho,Andreas
Schnizler,Björn
Personalized Information Access in a Bibliographic Peer-to-Peer System
Springer
143–158
2006
Hoser,Bettina
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
Semantic Network Analysis of Ontologies
Springer
4011
514-529
2006
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
BibSonomy: A Social Bookmark and Publication Sharing System
87-102
2006
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
Emergent Semantics in BibSonomy
P-94
2006
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
FolkRank: A Ranking Algorithm for Folksonomies
111-114
2006
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,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
Information Retrieval in Folksonomies: Search and Ranking
Springer
4011
411-426
2006
Hotho,Andreas
Jäschke,Robert
Schmitz,Christoph
Stumme,Gerd
Trend Detection in Folksonomies
Springer
4306
56-70
2006
Jäschke,Robert
Hotho,Andreas
Schmitz,Christoph
Ganter,Bernhard
Stumme,Gerd
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices
2006
Jäschke,Robert
Hotho,Andreas
Schmitz,Christoph
Stumme,Gerd
Wege zur Entdeckung von Communities in Folksonomies
Martin-Luther-Universität
80-84
2006
Schmitz,Christoph
Hotho,Andreas
J\"aschke,Robert
Stumme,Gerd
Content Aggregation on Knowledge Bases using Graph Clustering
Springer
4011
530-544
2006
Schmitz,Christoph
Hotho,Andreas
Jäschke,Robert
Stumme,Gerd
Kollaboratives Wissensmanagement
Springer
273-290
2006
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.
Schmitz,Christoph
Hotho,Andreas
Jäschke,Robert
Stumme,Gerd
Mining Association Rules in Folksonomies
Springer
261-270
2006
Stumme,Gerd
Hotho,Andreas
Berendt,Bettina
Semantic Web Mining - State of the Art and Future Directions
Journal of Web Semantics
Elsevier
4
124-143
2006
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.
Mejias,UlisesAli
A del.icio.us study
2004