Knowledge Discovery Enhanced with Semantic and Social Information

Bettina Berendt

ISBN 10: 3642426093 / ISBN 13: 9783642426094

Published by Springer Berlin Heidelberg Nov 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 160 pp. Englisch.

Seller Inventory # 9783642426094

Bibliographic Details

• Title: Knowledge Discovery Enhanced with Semantic and Social Information
• Publisher: Springer Berlin Heidelberg Nov 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3642426093
• ISBN 13: 9783642426094
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

From the Back Cover

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.