Bridging the Gap-Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.
Web Semantics: Science, Services and Agents on the World Wide Web, 8(2-3):95 - 96, 2010.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Bridging the Gap-Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.
Web Semantics: Science, Services and Agents on the World Wide Web, 8(2-3):95 - 96, 2010.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Bridging the Gap-Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.
Web Semantics: Science, Services and Agents on the World Wide Web, 8(2-3):95 - 96, 2010.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Bridging the Gap-Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.
Web Semantics: Science, Services and Agents on the World Wide Web, 8(2-3):95 - 96, 2010.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Intelligent scientific authoring tools: interactive data mining for constructive uses of citation networks.
Information Processing & Management, 46(1):1-10, 2010.
Bettina Berendt, Beate Krause und Sebastian Kolbe-Nusser.
[doi]
[BibTeX]
Research Challenges in Ubiquitous Knowledge Discovery.
In:
Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series), 1. Auflage.
Chapman & Hall/CRC, 2008.
Michael May, Bettina Berendt, Antoine Cornuéjols, Jõao Gama, Fosca Giannotti, Andreas Hotho, Donato Malerba, Ernestina Menesalvas, Katharina Morik, Rasmus Pedersen, Lorenza Saitta, Yücel Saygin, Assaf Schuster und Koen Vanhoof.
[doi]
[BibTeX]
Topical Structure discovery in Folksonomies.
In:
Proceedings of Wikis, Blogs, Bookmarking Tools - Mining the Web 2.0 Workshop (WBBTMine2008).
2008.
Ilija Subasic und Bettina Berendt.
[doi]
[BibTeX]
Semantics, Web and Mining.
2006.
Markus Ackermann, Bettina Berendt, Marko Grobelnik, Andreas Hotho, Dunja Mladenic, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtech Svatek und Maarten van Someren.
[doi]
[BibTeX]
Semantic Web Mining - State of the Art and Future Directions.
Journal of Web Semantics, 4(2):124-143, 2006.
Gerd Stumme, Andreas Hotho und Bettina Berendt.
[doi]
[Kurzfassung]
[BibTeX]
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.
Semantic Web Mining - State of the Art and Future Directions.
Journal of Web Semantics, 4(2):124-143, 2006.
Gerd Stumme, Andreas Hotho und Bettina Berendt.
[doi]
[Kurzfassung]
[BibTeX]
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.
Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.
In: V. Svatek und V. Snasel
(Herausgeber):
Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space, Seiten 1-16.
Technical University of Ostrava, 2005.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.
In: V. Svatek und V. Snasel
(Herausgeber):
Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space, Seiten 1-16.
Technical University of Ostrava, 2005.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.
In: V. Svatek und V. Snasel
(Herausgeber):
Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space, Seiten 1-16.
Technical University of Ostrava, 2005.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.
In: V. Svatek und V. Snasel
(Herausgeber):
Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space, Seiten 1-16.
Technical University of Ostrava, 2005.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[BibTeX]
A Roadmap for Web Mining: From Web to Semantic Web..
In: B. Berendt, A. Hotho, D. Mladenic, M. van Someren, M. Spiliopoulou und G. Stumme
(Herausgeber):
Web Mining: From Web to Semantic Web, Band 3209, Seiten 1-22.
Springer, Heidelberg, 2004.
Bettina Berendt, Andreas Hotho, Dunja Mladenic, Maarten van Someren, Myra Spiliopoulou und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
A Roadmap for Web Mining: From Web to Semantic Web..
In: B. Berendt, A. Hotho, D. Mladenic, M. van Someren, M. Spiliopoulou und G. Stumme
(Herausgeber):
Web Mining: From Web to Semantic Web, Band 3209, Seiten 1-22.
Springer, Heidelberg, 2004.
Bettina Berendt, Andreas Hotho, Dunja Mladenic, Maarten van Someren, Myra Spiliopoulou und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Usage Mining for and on the Semantic Web.
In:
H. Kargupta, A. Joshi, K. Sivakumar und Y. Yesha (Herausgeber):
Data Mining Next Generation Challenges and Future Directions, Seiten 461-481.
AAAI Press, Boston, 2004.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Usage Mining for and on the Semantic Web.
In:
H. Kargupta, A. Joshi, K. Sivakumar und Y. Yesha (Herausgeber):
Data Mining Next Generation Challenges and Future Directions, Seiten 461-481.
AAAI Press, Boston, 2004.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Usage Mining for and on the Semantic Web.
In:
H. Kargupta, A. Joshi, K. Sivakumar und Y. Yesha (Herausgeber):
Data Mining Next Generation Challenges and Future Directions, Seiten 461-481.
AAAI Press, Boston, 2004.
Bettina Berendt, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Conceptual User Tracking.
In: E. M. Ruiz, J. Segovia und P. S. Szczepaniak
(Herausgeber):
Advances in Web Intelligence, First International Atlantic Web Intelligence Conference, AWIC 2003, Madrid, Spain, May 5-6, 2003, Proceedings, Band 2663, Reihe Lecture Notes in Artificial Intelligence, Seiten 142-154.
Springer, 2003.
Daniel Oberle, Bettina Berendt, Andreas Hotho und Jorge Gonzalez.
[doi]
[BibTeX]