Knowledge Discovery Enhanced with Semantic and Social Information / Edition 1

Knowledge Discovery Enhanced with Semantic and Social Information / Edition 1

by Bettina Berendt
     
 

ISBN-10: 3642018904

ISBN-13: 9783642018909

Pub. Date: 06/25/2009

Publisher: Springer Berlin Heidelberg

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

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Overview

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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Product Details

ISBN-13:
9783642018909
Publisher:
Springer Berlin Heidelberg
Publication date:
06/25/2009
Series:
Studies in Computational Intelligence Series, #220
Edition description:
2009
Pages:
143
Product dimensions:
9.21(w) x 6.14(h) x 0.44(d)

Table of Contents

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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