Concept learning for ℰℒ <sup>++</sup> by refinement and reinforcement

Chitsaz, M; Wang, K; Blumenstein, M; Qi, G

Concept learning for ℰℒ <sup>++</sup> by refinement and reinforcement

Chitsaz, M Wang, K Blumenstein, M

Qi, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7458 LNAI pp. 15 - 26
Issue Date:: 2012-10-25

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Field	Value	Language
dc.contributor.author	Chitsaz, M	en_US
dc.contributor.author	Wang, K	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Qi, G	en_US
dc.date.issued	2012-10-25	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7458 LNAI pp. 15 - 26	en_US
dc.identifier.isbn	9783642326943	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120997
dc.description.abstract	Ontology construction in OWL is an important and yet time-consuming task even for knowledge engineers and thus a (semi-) automatic approach will greatly assist in constructing ontologies. In this paper, we propose a novel approach to learning concept definitions in from a collection of assertions. Our approach is based on both refinement operator in inductive logic programming and reinforcement learning algorithm. The use of reinforcement learning significantly reduces the search space of candidate concepts. Besides, we present an experimental evaluation of constructing a family ontology. The results show that our approach is competitive with an existing learning system for ℰℒ. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-32695-0_4	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Concept learning for ℰℒ <sup>++</sup> by refinement and reinforcement	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7458 LNAI	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7458 LNAI	en_US

Abstract:

Ontology construction in OWL is an important and yet time-consuming task even for knowledge engineers and thus a (semi-) automatic approach will greatly assist in constructing ontologies. In this paper, we propose a novel approach to learning concept definitions in from a collection of assertions. Our approach is based on both refinement operator in inductive logic programming and reinforcement learning algorithm. The use of reinforcement learning significantly reduces the search space of candidate concepts. Besides, we present an experimental evaluation of constructing a family ontology. The results show that our approach is competitive with an existing learning system for ℰℒ. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/120997