An Abductive Framework for Datalog± Ontologies

Ontologies are a fundamental component of the Semantic Web since they provide a formal and machine manipulable model of a domain. Description Logics (DLs) are often the languages of choice for modeling ontologies. Great effort has been spent in identifying decidable or even tractable fragments of DLs. Conversely, for knowledge representation and reasoning, integration with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision. Datalog ± is an extension of Datalog which can be used for representing lightweight ontologies, and is able to express the DL-Lite family of ontology languages, with tractable query answering under certain language restrictions. In this work, we show that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog ± ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning. In particular, we consider an Abductive Logic Programming framework named SCIFF and derived from the IFF abductive framework, able to deal with existentially (and universally) quantified variables in rule heads, and Constraint Logic Programming constraints. Forward and backward reasoning is naturally supported in the ALP framework. We show that the SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog± ontologies, mapped into SCIFF (forward) integrity constraints.