PEPS-RegleX-LD in contracts (2024-02-10)

Ontology matching and data interlinking as distinguished tasks aim at facilitating the interoperability between different knowledge bases. Although the field has fully developed in the last years, most works still focus on generating simple correspondences between entities. These correspondences are however insufficient to fully cover the different types of heterogeneity between the knowledge base and complex correspondences are therefore required. Compared to simple matching, few approaches for complex matching have been proposed, focusing on correspondence patterns or exploiting common instances between the ontologies. Similarly, unsupervised data interlinking approaches (which do not require labelled data samples) have recently been developed. One approach consists in discovering linking rules such as simple keys or conditional keys on unlabelled data. The results have shown that the more expressive the rules, the higher the recall. Even more expressive rules (referential expressions, graph keys, etc.) are rather required, however naive approaches to the discovery of these rules can not be envisaged on large data sets. Existing approaches presuppose either that the data conform to the same ontology or that all possible pairs of properties be examined. Complementary, link keys are a set of pairs of properties that identify the instances of two classes of two RDF datasets. Such, link keys may be directly extracted without the need for an alignment. We introduce here an approach that aims at evaluating the impact of complex correspondences in the task of data interlinking established from the application of keys.