An improved MLMA+ and its application in ontology matching

Ontology matching is one of the main aspects of semantic Web, which aims at finding correspondences between entities of given ontologies. These found correspondences can be used for applications such as ontology merging, query rewriting, instance transformation, and so on. There has been proposed some algorithms and techniques for matching ontologies. One of these algorithms is multi-level matching algorithm plus (MLMA+) algorithm which has been recently proposed. In this paper we introduced a similarity measure based on Levenshtein measure used in MLMA+ and also made some enhancements to MLMA+ algorithm´s neighbor search technique to improve it and enhance its result´s quality in ontology matching task. These improvements in the neighbor search make it more efficient and increase its performance by reducing both memory usage and computation time specifically in matching large ontologies.