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A goal-directed implementation of query answering for hybrid MKNF knowledge bases

Published online by Cambridge University Press:  18 January 2013

ANA SOFIA GOMES
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: [email protected], [email protected], [email protected])
JOSÉ JÚLIO ALFERES
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: [email protected], [email protected], [email protected])
TERRANCE SWIFT
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: [email protected], [email protected], [email protected])

Abstract

Ontologies and rules are usually loosely coupled in knowledge representation formalisms. In fact, ontologies use open-world reasoning, while the leading semantics for rules use non-monotonic, closed-world reasoning. One exception is the tightly coupled framework of Minimal Knowledge and Negation as Failure (MKNF), which allows statements about individuals to be jointly derived via entailment from ontology and inferences from rules. Nonetheless, the practical usefulness of MKNF has not always been clear, although recent work has formalized a general resolution-based method for querying MKNF when rules are taken to have the well-founded semantics, and the ontology is modeled by a general oracle. That work leaves open what algorithms should be used to relate the entailments of the ontology and the inferences of rules. In this paper we provide such algorithms, and describe the implementation of a query-driven system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) rules under the well-founded semantics and a (monotonic) ontology, represented by the Coherent Description Framework Type-1 ( $\mathcal{ALCQ}$ ) theory.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2013 

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