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 - Strube, Michael AU - Ponzetto, Simone Paolo A2 - T1 - WikiRelate! computing semantic relatedness using wikipedia T2 - proceedings of the 21st national conference on Artificial intelligence - Volume 2 PB - AAAI Press CY - PY - 2006/ M2 - VL - IS - SP - 1419 EP - 1424 UR - http://dl.acm.org/citation.cfm?id=1597348.1597414 M3 - KW - measure KW - relatedness KW - semantic KW - similarity KW - wikipedia KW - wordnet L1 - SN - 978-1-57735-281-5 N1 - WikiRelate! computing semantic relatedness using wikipedia N1 - AB - Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts. 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 M3 - 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 - TY - CONF AU - Berendt, Bettina AU - Hotho, Andreas AU - Mladenic, Dunja AU - van Someren, Maarten AU - Spiliopoulou, Myra AU - Stumme, Gerd A2 - Berendt, Bettina A2 - Hotho, Andreas A2 - Mladenic, Dunja A2 - van Someren, Maarten A2 - Spiliopoulou, Myra A2 - Stumme, Gerd T1 - A Roadmap for Web Mining: From Web to Semantic Web. T2 - Web Mining: From Web to Semantic Web PB - Springer CY - Heidelberg PY - 2004/ M2 - VL - 3209 IS - SP - 1 EP - 22 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2004/berendt2004roadmap.pdf M3 - KW - 2004 KW - ecml KW - ewmf KW - itegpub KW - l3s KW - mining KW - myown KW - pkdd KW - proceedings KW - roadmap KW - semantic KW - web KW - workshop L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science. ER - TY - CHAP AU - Berendt, Bettina AU - Hotho, Andreas AU - Stumme, Gerd A2 - Kargupta, Hillol A2 - Joshi, Anupam A2 - Sivakumar, Krishnamoorthy A2 - Yesha, Yelena T1 - Usage Mining for and on the Semantic Web T2 - Data Mining Next Generation Challenges and Future Directions PB - AAAI Press CY - Boston PY - 2004/ VL - IS - SP - 461 EP - 481 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2004/berendt04usage.pdf M3 - KW - 2004 KW - itegpub KW - l3s KW - mining KW - myown KW - semantic KW - usage KW - web L1 - SN - 0-262-61203-8 N1 - Publications of Gerd Stumme N1 - AB - Semantic Web Mining aims at combining the two fast-developing
research areas Semantic Web and Web Mining.
Web Mining aims at discovering insights about the meaning of Web
resources and their usage. Given the primarily syntactical nature
of data Web mining operates on, the discovery of meaning is
impossible based on these data only. Therefore, formalizations of
the semantics of Web resources and navigation behavior are
increasingly being used. This fits exactly with the aims of the
Semantic Web: the Semantic Web enriches the WWW by
machine-processable information which supports the user in his
tasks. In this paper, we discuss the interplay of the Semantic Web
with Web Mining, with a specific focus on usage mining. ER - TY - GEN AU - A2 - Berendt, Bettina A2 - Hotho, Andreas A2 - Mladenic, Dunja A2 - van Someren, Maarten A2 - Spiliopoulou, Myra A2 - Stumme, Gerd T1 - Web Mining: From Web to Semantic Web, First European Web
Mining Forum, EMWF 2003, Cavtat-Dubrovnik, Croatia, September
22, 2003, Revised Selected and Invited Papers JO - PB - Springer AD - Heidelberg PY - 2004/ VL - 3209 IS - SP - EP - UR - http://springerlink.metapress.com/content/unvvag26dttf/ M3 - KW - 2004 KW - ecml KW - itegpub KW - l3s KW - mining KW - myown KW - pkdd KW - proceedings KW - semantic KW - web KW - workshop L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Tane, Julien AU - Schmitz, Christoph AU - Stumme, Gerd A2 - T1 - Semantic resource management for the web: an e-learning application T2 - Proc. 13th International World Wide Web Conference (WWW 2004) PB - CY - PY - 2004/ M2 - VL - IS - SP - 1 EP - 10 UR - http://www.www2004.org/proceedings/docs/2p1.pdf M3 - KW - 2004 KW - application KW - courseware KW - crawler KW - e-learning KW - eLearning KW - edutella KW - fca KW - itegpub KW - l3s KW - learning KW - myown KW - p2p KW - semantic KW - watchdog KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Agarwal, Sudhir AU - Fankhauser, Peter AU - Gonzalez-Ollala, Jorge AU - Hartmann, Jens AU - Hollfelder, Silvia AU - Jameson, Anthony AU - Klink, Stefan AU - Lehti, Patrick AU - Ley, Michael AU - Rabbidge, Emma AU - Schwarzkopf, Eric AU - Shrestha, Nitesh AU - Stojanovic, Nenad AU - Studer, Rudi AU - Stumme, Gerd AU - Walter, Bernd A2 - Dittrich, K. A2 - König, W. A2 - Oberweis, A. A2 - Rannenberg, K. A2 - Wahlster, W. T1 - Semantic Methods and Tools for Information Portals T2 - INFORMATIK 2003 -- Innovative Informatikanwendungen (Band 1) PB - Gesellschaft für Informatik CY - Bonn PY - 2003/ M2 - VL - 34 IS - SP - 116 EP - 131 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2003/agarwal2003semantic.pdf M3 - KW - 2003 KW - information KW - informationsportale KW - myown KW - ontologies KW - portals KW - semantic KW - semiport KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - The paper describes a set of approaches for representing and
accessing information within a semantically structured information
portal, while offering the possibility to integrate own
information. It discusses research performed within the project
`Semantic Methods and Tools for Information Portals (SemIPort)'.
In particular, it focuses on (1) the development of scalable
storing, processing and querying methods for semantic data, (2)
visualization and browsing of complex data inventories, (3)
personalization and agent-based interaction, and (4) the
enhancement of web mining approaches for use within a
semantics-based portal. ER - TY - RPRT AU - Hotho, Andreas AU - Staab, Steffen AU - Stumme, Gerd A2 - T1 - Text Clustering Based on Background Knowledge PB - University of Karlsruhe, Institute AIFB AD - PY - 2003/ VL - 425 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf M3 - KW - 2003 KW - analysis KW - background KW - clustering KW - concept KW - fca KW - formal KW - knowledge KW - myown KW - ontologies KW - semantic KW - text KW - web L1 - N1 - Publications of Gerd Stumme N1 - Technical Report N1 - AB - Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results. ER - TY - GEN AU - A2 - Berendt, B. A2 - Hotho, A. A2 - Stumme, G. T1 - Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf. on
Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and
Practice of Knowledge Discovery in Databases (PKDD'02) JO - PB - AD - Helsinki, Finland PY - 2002/august 19, VL - IS - SP - EP - UR - http://km.aifb.uni-karlsruhe.de/ws/semwebmine2002/online_html M3 - KW - 2002 KW - ecml KW - mining KW - myown KW - pkdd KW - proceedings KW - semantic KW - web KW - workshop L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Berendt, B. AU - Hotho, A. AU - Stumme, G. A2 - Horrocks, I. A2 - Hendler, J. T1 - Towards Semantic Web Mining T2 - The Semantic Web -- ISWC 2002 PB - Springer CY - Heidelberg PY - 2002/ M2 - VL - IS - SP - 264 EP - 278 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/ISWC02.pdf M3 - KW - 2002 KW - mining KW - myown KW - semantic KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Bozsak, E. AU - Ehrig, Marc AU - Handschuh, Siegfried AU - Hotho, Andreas AU - Maedche, Alexander AU - Motik, Boris AU - Oberle, Daniel AU - Schmitz, Christoph AU - Staab, Steffen AU - Stojanovic, Ljiljana AU - Stojanovic, Nenad AU - Studer, Rudi AU - Stumme, Gerd AU - Sure, York AU - Tane, Julien AU - Volz, Raphael AU - Zacharias, Valentin A2 - Bauknecht, Kurt A2 - Tjoa, A. Min A2 - Quirchmayr, Gerald T1 - KAON - Towards a large scale Semantic Web T2 - Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France PB - Springer CY - PY - 2002/ M2 - VL - 2455 IS - SP - 304 EP - 313 UR - http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2002_ecweb_kaon.pdf M3 - KW - 2002 KW - aifb KW - kaon KW - karlsruhe KW - l3s KW - myown KW - ontologies KW - semantic KW - social KW - web L1 - SN - N1 - Institut AIFB - Publikationen N1 - AB - ER - TY - CONF AU - Gonzalez-Olalla, J. AU - Stumme, G. A2 - Berendt, B. A2 - Hotho, A. A2 - Stumme, G. T1 - Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description) T2 - Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf. PB - CY - Helsinki PY - 2002/august 19, M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/gonzalez2002semantic.pdf M3 - KW - 2002 KW - Semantic KW - bmbf KW - information KW - informationsportale KW - myown KW - ontologies KW - portal KW - portals KW - project KW - semantic KW - semiport KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Stumme, G. AU - Berendt, B. AU - Hotho, A. A2 - T1 - Usage Mining for and on the Semantic Web T2 - Proc. NSF Workshop on Next Generation Data Mining PB - CY - Baltimore PY - 2002/november M2 - VL - IS - SP - 77 EP - 86 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/NSF-NGDM02.pdf M3 - KW - 2002 KW - mining KW - myown KW - semantic KW - usage KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CHAP AU - Stumme, G. A2 - Becker, J. A2 - Knackstedt, R. T1 - Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge T2 - Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung PB - Physica CY - Heidelberg PY - 2002/ VL - IS - SP - 163 EP - 174 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2001/REFMOD01.ps M3 - KW - 2002 KW - analysis KW - business KW - concept KW - fca KW - formal KW - knowledge KW - management KW - myown KW - ontologies KW - semantic KW - web KW - wissensmanagement L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER -