Ontologies for Machine Learning

Stephan Bloehdorn () and Andreas Hotho ()
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Stephan Bloehdorn: Institute AIFB, University of Karlsruhe
Andreas Hotho: Department of Mathematics and Computer Science, University of Kassel

A chapter in Handbook on Ontologies, 2009, pp 637-661 from Springer

Abstract: 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.

Keywords: Resource Description Framework; Description Logic; Inductive Logic Programming; Word Sense Disambiguation; Formal Concept Analysis (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ihichp:978-3-540-92673-3_29

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DOI: 10.1007/978-3-540-92673-3_29

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