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2009 | OriginalPaper | Buchkapitel

Ontologies for Machine Learning

verfasst von : Stephan Bloehdorn, Andreas Hotho

Erschienen in: Handbook on Ontologies

Verlag: Springer Berlin Heidelberg

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Summary

The growing amounts of ontologies and semantically annotated data has led to considerable interest in mining these richly structured data sources. While research has actively addressed the issue of inducing semantic structures from conventional types of data, approaches for mining semantically annotated data still constitute an emerging field of research. Approaches in this direction either investigate how semantic structures can help to advance classical Machine Learning tasks or how semantic structures can themselves become the objects of interest. In this chapter, we review some of the main topics at the intersection of Machine Learning and Semantic Web research.

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Titel: Ontologies for Machine Learning
verfasst von: Stephan Bloehdorn
Andreas Hotho
Verlag: Springer Berlin Heidelberg
Buch: Handbook on Ontologies
Print ISBN: 978-3-540-70999-2

Electronic ISBN: 978-3-540-92673-3

Copyright-Jahr: 2009
DOI: https://doi.org/10.1007/978-3-540-92673-3_29

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