Overview
- Presents latest results on knowledge discovery enhanced with semantic and social information
Part of the book series: Studies in Computational Intelligence (SCI, volume 220)
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Table of contents (8 chapters)
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Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery
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Web Mining 2.0
Keywords
About this book
There is general agreement that the effectiveness of Machine Learning and Knowledge Discovery output strongly depends not only on the quality of source data and the sophistication of learning algorithms, but also on additional input provided by domain experts. There is less agreement on whether, when and how such input can and should be formalized as explicit prior knowledge.
The six chapters in the first part of the book aim to investigate this aspect by addressing four different topics: inductive logic programming; the role of human users; investigations of fully automated methods for integrating background knowledge; the use of background knowledge for Web mining. The two chapters in the second part are motivated by the Web 2.0 (r)evolution and the increasingly strong role of user-generated content. The contributions emphasize the vision of the Web as a social medium for content and knowledge sharing.
Editors and Affiliations
Bibliographic Information
Book Title: Knowledge Discovery Enhanced with Semantic and Social Information
Editors: Bettina Berendt, Dunja Mladenič, Marco Gemmis, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtěch Svátek, … Filip Železný
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-01891-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-642-01890-9Published: 29 June 2009
Softcover ISBN: 978-3-642-42609-4Published: 06 November 2014
eBook ISBN: 978-3-642-01891-6Published: 09 July 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 143
Topics: Computational Intelligence, Artificial Intelligence, Semantics