Enriching Ontology-based Data Access with Provenance

Diego Calvanese, Davide Lanti, Ana Ozaki, Rafael Peñaloza, and Guohui Xiao

Proc. of the 28th Int. Joint Conf. on Artificial Intelligence (IJCAI 2019). 2019.

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-Lite 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.


@inproceedings{IJCAI-2019,
   title = "Enriching Ontology-based Data Access with Provenance",
   year = "2019",
   author = "Diego Calvanese and Davide Lanti and Ana Ozaki and Rafael
Peñaloza and Guohui Xiao",
   booktitle = "Proc. of the 28th Int. Joint Conf. on Artificial Intelligence
(IJCAI 2019)",
   pages = "1616--1623",
   publisher = "Int. Joint Conf. on Artificial Intelligence Organization",
   doi = "10.24963/ijcai.2019/224",
}
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