Ontology mapping is a key problem to be solved for the success of the Semantic Web and related technologies. An ontology mapping algorithm aims at finding correspondences (or mappings) between entities of the source and target ontologies by combining several matching components, i.e., individual matchers, that exploit one or more sources of information encoded within the ontologies. In this paper, we investigate linguistic techniques for ontology mapping and underline their importance in paving the way to other matching techniques. We define a general mapping model architecture and discuss an implementation in the Lucene ontology matcher (LOM). LOM leverages the features of the Lucene search engine library. The basic idea is to gather the different kinds of linguistic information of the source ontology entities in Lucene documents that will be stored into an index. Mappings are discovered by using the values of entities in the target ontology as search arguments against the index created from the source ontology. Extensive experimental results using a popular benchmark test suite show the effectiveness of this approach in terms of precision, recall, F-measure and execution time as compared to other linguistic approaches.
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