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Knowledge Discovery Enhanced with Semantic and Social Information
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Gewicht:
254 g
Format:
235x155x9 mm
Beschreibung:
Prof. Dr. rer. nat. Gerd Stumme leitet seit 2004 den Hertie-Lehrstuhl für Wissensverarbeitung im Forschungszentrum für Informationstechnik-Gestaltung (ITeG) der Universität Kassel und ist Mitglied im Forschungszentrum L3S. Er promovierte 1997 an der TU Darmstadt und habilitierte 2003 an der Universität Karlsruhe. Gerd Stumme veröffentlichte über 120 Beiträge in nationalen und internationalen Tagungen und Zeitschriften und war Chair verschiedener Workshops und Tagungen.
This book is showcases recent advances in knowledge discovery enhanced with semantic and social information. It includes eight chapters that grew out of joint workshops at ECML/PKDD 2007. The contributions emphasize the vision of the Web as a social medium.
Presents latest results on knowledge discovery enhanced with semantic and social information
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.
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.
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.