Enriching Ontology-based Data Access with Provenance

Enriching Ontology-based Data Access with Provenance

Diego Calvanese, Davide Lanti, Ana Ozaki, Rafael Penaloza, Guohui Xiao

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1616-1623. https://doi.org/10.24963/ijcai.2019/224

Ontology-based data access (OBDA) is a popular paradigm for querying heterogeneous data sources by connecting them through mappings to an ontology. In OBDA, it is often difficult to reconstruct why a tuple occurs in the answer of a query. We address this challenge by enriching OBDA with provenance semirings, taking inspiration from database theory. In particular, we investigate the problems of (i) deciding whether a provenance annotated OBDA instance entails a provenance annotated conjunctive query, and (ii) computing a polynomial representing the provenance of a query entailed by a provenance annotated OBDA instance. Differently from pure databases, in our case, these polynomials may be infinite. To regain finiteness, we consider idempotent semirings, and study the complexity in the case of DL-LiteR ontologies. We implement Task (ii) in a state-of-the-art OBDA system and show the practical feasibility of the approach through an extensive evaluation against two popular benchmarks.
Keywords:
Knowledge Representation and Reasoning: Computational Complexity of Reasoning
Knowledge Representation and Reasoning: Description Logics and Ontologies
Knowledge Representation and Reasoning: Logics for Knowledge Representation