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2018 | OriginalPaper | Chapter

DLFoil: Class Expression Learning Revisited

Authors : Nicola Fanizzi, Giuseppe Rizzo, Claudia d’Amato, Floriana Esposito

Published in: Knowledge Engineering and Knowledge Management

Publisher: Springer International Publishing

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Abstract

The paper presents the ultimate version of a concept learning system which can support typical ontology construction/evolution tasks through the induction of class expressions from groups of individual resources labeled by a domain expert. Stating the target task as a search problem, a Foil-like algorithm was devised based on the employment of refinement operators to traverse the version-space of candidate definitions for the target class. The algorithm has been further enhanced including a more general definition for the scoring function and better refinement operators. An experimental evaluation of the resulting new release of DL-Foil, which implements these improvements was carried out to assess its performance also in comparison with other concept learning systems.

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previous chapter A Framework for Explaining Query Answers in DL-Lite

next chapter Requirements Behaviour Analysis for Ontology Testing

A space endowed with a quasi-ordering, i.e. a reflexive and transitive relationship.

This may be considered a basic upper refinement operator allowed by expressive DL languages (encompassing \(\mathcal {ALC}\)).

An acyclic TBox does not contains multiple definitions for a concept name and such a concept is not used to the right-side of an equivalence axiom.

The source code and the datasets/ontologies are publicly available at: https://bitbucket.org/grizzo001/dl-foil/src/master/.

Assessed by JFact reasoner: http://jfact.sourceforge.net/.

The experiments were carried out on a 8-core Ubuntu server with 16 GB RAM.

spark.apache.org.

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Title: DLFoil: Class Expression Learning Revisited
Authors: Nicola Fanizzi
Giuseppe Rizzo
Claudia d’Amato
Floriana Esposito
Publisher: Springer International Publishing
Book: Knowledge Engineering and Knowledge Management
Print ISBN: 978-3-030-03666-9

Electronic ISBN: 978-3-030-03667-6

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03667-6_7

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