Knowledge discovery enhanced with semantic and social information

This book is a showcase of recent advances in knowledge discovery enhanced with semantic and social information. It includes eight contributed chapters that grew out of two joint workshops at ECML/PKDD 2007. There is general agreement that the effectiveness of Machine Learning and Knowledge Discover...

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主要作者: Kacprzyk, Janusz, 1947-
其他作者: Berendt, Bettina, 19..- (Directeur de la publication), Gemmis, Marco de (Directeur de la publication), Mladenić, Dunja, 1967- (Directeur de la publication), Semeraro, Giovanni (Directeur de la publication), Spiliopoulou, Myra, 1965- (Directeur de la publication), Stumme, Gerd, 1967- (Directeur de la publication), Svátek, Vojtěch (Directeur de la publication), Železný, Filip (Directeur de la publication)
格式: Livre numérique
語言:Anglais
出版: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
版:1st ed. 2009.
叢編:Studies in Computational Intelligence 220
在線閱讀:Accès sur la plateforme de l'éditeur
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Edition sous un autre format:• Knowledge discovery enhanced with semantic and social information, Bettina Berendt, Dunja Mladenič, Marco de gemmis... [et al.], eds., Berlin, Springer, 2009, 1 vol. (X-143 p.), Studies in computational intelligence, 978-3-642-01890-9
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505 1 |a Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery On Ontologies as Prior Conceptual Knowledge in Inductive Logic Programming A Knowledge-Intensive Approach for Semi-automatic Causal Subgroup Discovery A Study of the SEMINTEC Approach to Frequent Pattern Mining Partitional Conceptual Clustering of Web Resources Annotated with Ontology Languages The Ex Project: Web Information Extraction Using Extraction Ontologies Dealing with Background Knowledge in the SEWEBAR Project Web Mining 2.0 Item Weighting Techniques for Collaborative Filtering Using Term-Matching Algorithms for the Annotation of Geo-services 
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520 |a This book is a showcase of recent advances in knowledge discovery enhanced with semantic and social information. It includes eight contributed chapters that grew out of two joint workshops at ECML/PKDD 2007. 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 
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700 1 |a Gemmis, Marco de.  |4 pbd 
700 1 |a Mladenić, Dunja,  |d 1967-  |4 pbd 
700 1 |a Semeraro, Giovanni.  |4 pbd 
700 1 |a Spiliopoulou, Myra,  |d 1965-  |4 pbd 
700 1 |a Stumme, Gerd,  |d 1967-  |4 pbd 
700 1 |a Svátek, Vojtěch.  |4 pbd 
700 1 |a Železný, Filip.  |4 pbd 
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