Efficient Ontology-Based Data Integration with Canonical IRIs

Proc. of the 15th Extended Semantic Web Conf. (ESWC 2018). Volume 10843 of Lecture Notes in Computer Science. 2018.

In this paper, we study how to efficiently integrate multiple relational databases using an ontology-based approach. In ontology-based data integration (OBDI) an ontology provides a coherent view of multiple databases, and SPARQL queries over the ontology are rewritten into (federated) SQL queries over the underlying databases. Specifically, we address the scenario where records with different identifiers in different databases can represent the same entity. The standard approach in this case is to use sameAs to model the equivalence between entities. However, the standard semantics of sameAs may cause an exponential blow up of query results since all possible combinations of equivalent identifiers have to be included in the answers. The large number of answers is not only detrimental to the performance of query evaluation, but also makes the answers difficult to understand due to the redundancy they introduce. This motivates us to propose an alternative approach, which is based on assigning canonical IRIs to entities in order to avoid redundancy. Formally, we present our approach as a new SPARQL entailment regime and compare it with the sameAs approach. We provide a prototype implementation and evaluate it in two experiments: in a real-world data integration scenario in Statoil and in an experiment extending the Wisconsin benchmark. The experimental results show that the canonical IRI approach is significantly more scalable.

@inproceedings{ESWC-2018,
   title = "Efficient Ontology-Based Data Integration with Canonical IRIs",
   year = "2018",
   author = "Guohui Xiao and Dag Hovland and Dimitris Bilidas and Martin
Rezk and Martin Giese and Diego Calvanese",
  booktitle = "Proc. of the 15th Extended Semantic Web Conf. (ESWC 2018)",
   pages = "697--713",
   volume = "10843",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-319-93417-4_45",
}