String similarity metrics for ontology alignment

Abstract

Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned. © 2013 Springer-Verlag.