Abstract
Simple ontology alignments, largely studied, link one entity of a source ontology to one entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. Although different complex matching approaches have emerged in the literature, there is a lack of complex reference alignments on which these approaches can be systematically evaluated. This paper proposes two sets of complex alignments between 10 pairs of ontologies from the well-known OAEI conference simple alignment dataset.
Highlights
Ontology matching is an essential task for the management of the semantic heterogeneity in open environments
While approaches generating simple correspondences are limited in expressiveness by linking single entities, complex matching approaches are able to generate correspondences which better express the relationships between entities of different ontologies
This paper proposes two alignment sets to extend the Ontology Alignment Evaluation Campaigns (OAEI) conference track dataset [3,36] with complex alignments for two task purposes: ontology merging and query rewriting
Summary
Ontology matching is an essential task for the management of the semantic heterogeneity in open environments. The matching process aims at generating a set of correspondences (i.e., an alignment) between the entities of different ontologies. Different approaches for generating such complex alignments have been proposed in the literature. While the proposal of [23,24] relies on correspondence patterns, the one in [13] uses knowledge-rules in Markov-Logic Networks. Despite the progress in the field, there is a lack of reference alignment sets on which the complex approaches can be systematically evaluated. Systematic evaluation of them has been carried out over the last fifteen years in the context of the Ontology Alignment Evaluation Campaigns (OAEI)1 Even though this well-known campaign proposes a task-oriented benchmark (the OA4QA track [28]), it does not propose a complex alignment benchmark. We consider two types of correspondences depending on the type of their members:
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