UFOMQ: An Algorithm for Querying for Similar Individuals in Heterogeneous Ontologies

The chief challenge in identifying similar individuals across multiple ontologies is the high computational cost of evaluating similarity between every pair of entities. We present an approach to querying for similar individuals across multiple ontologies that makes use of the correspondences discovered during ontology alignment in order to reduce this cost. The query algorithm is designed using the framework of fuzzy logic and extends fuzzy ontology alignment. The algorithm identifies entities that are related to the given entity directly from a single alignment link or by following multiple alignment links. We evaluate the approach using both publicly available ontologies and from an enterprise-scale dataset. Experiments show that it is possible to trade-off a small decrease in precision of the query results with a large savings in execution time.