A semantic similarity measure based on information distance for ontology alignment

Abstract

Ontology alignment is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the alignment between them before processing across them. Many efforts have been conducted to automate the alignment by discovering the correspondence between entities of ontologies. However, some problems are still obvious, and the most crucial one is that it is almost impossible to extract semantic meaning of a lexical label that denotes the entity by traditional methods. In this paper, ontology alignment is formalized as a problem of information distance metric. In this way, discovery of optimal alignment is cast as finding out the correspondences with minimal information distance. We demonstrate a novel measure named link weight that uses semantic characteristics of two entities and Google page count to calculate an information distance similarity between them. The experimental results show that our method is able to create alignments between different lexical entities that denotes the same ones. These results outperform the typical ontology alignment methods like PROMPT (Noy and Musen, 2000) [38], QOM (Ehrig and Staab, 2004) [12], and APFEL (Ehrig et al., 2005) [13] in terms of semantic precision and recall.