Coincidence-Based Scoring of Mappings in Ontology Alignment

Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By in- terpreting ontologies as Typed Graphs embedded in a Metric Space, coincidence of the structures of the two ontologies is formulated. Having such a formu- lation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straight- forward and capable of finding the optimum align- ment, investigates all possible alignments, but its run- time complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same pur- pose, measures random walks in each ontology versus the other.