Concept Induction in Description Logics Using Information-Theoretic Heuristics

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem...

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Veröffentlicht in:International journal on semantic web and information systems Jg. 7; H. 2; S. 23 - 44
1. Verfasser: Fanizzi, Nicola
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.04.2011
Schlagworte:
Algorithms
Descriptions
Heuristic
Heuristic programming
Heuristics
Information systems
Information theory
Knowledge representation
Learning
Logic
Machine learning
Ontology
Representations
Semantic web
Semantics
Italy
ISSN:1552-6283, 1552-6291
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Zusammenfassung:This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.
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ISSN:1552-6283
1552-6291
DOI:10.4018/jswis.2011040102