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THE DATALOG DL COMBINATION OF DEDUCTION RULES AND DESCRIPTION LOGICS
Author(s) -
Mei Jing,
Lin Zuoquan,
Boley Harold,
Li Jie,
Bhavsar Virendrakumar C.
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00311.x
Subject(s) - datalog , description logic , computer science , semantic reasoner , decidability , programming language , ruleml , web ontology language , semantic web , semantic web rule language , parameterized complexity , theoretical computer science , ontology language , artificial intelligence , xml , algorithm , world wide web , social semantic web , semantic analytics , sgml , efficient xml interchange
Uniting ontologies and rules has become a central topic in the Semantic Web. Bridging the discrepancy between these two knowledge representations, this paper introduces Datalog DL as a family of hybrid languages, where Datalog rules are parameterized by various DL (description logic) languages ranging from to . Making Datalog DL a decidable system with complexity of EXPTIME, we propose independent properties in the DL body as the restriction to hybrid rules, and weaken the safeness condition to balance the trade‐off between expressivity and reasoning power. Building on existing well‐developed techniques, we present a principled approach to enrich (RuleML) rules with information from (OWL) ontologies, and develop a prototype system combining a rule engine (OO jDREW) with a DL reasoner (RACER).