Datalog+- Ontology Consolidation

M. VANINA MARTÍNEZ; CRISTHIAN A. D. DEAGUSTINI; GUILLERMO R. SIMARI; MARCELO A. FALAPPA

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, THE

AI ACCESS FOUNDATION

Lugar: New York; Año: 2016 vol. 56 p. 613 - 656

1076-9757

Knowledge bases in the form of ontologies are receiving increasing attention as theyallow to clearly represent both the available knowledge,which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalogontologies are attractive because of their property of decidability and the possibility ofdealing with the massive amounts of data in real world environments; however, as it is thecase with many other ontological languages, when applied to collaborative environmentsoften lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog ontologies, in terms of satisability of sets of constraints, andshow how under specic conditions incoherence leads to inconsistent Datalog ontologies.The main contribution of this work is a novel approach to restore both consistency andcoherence in Datalog ontologies. The proposed approach is based on kernel contractionand restoration is performed by the application of incision functions that select formul todelete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satised by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.