Semantic Relatedness Measurement (SRM) is one of the most important applications of reasoning by ontologies and different disciplines of AI, e.g. Information Retrieval, are firmly tied to it. The accuracy of SRM by lexical resources is largely determined by the quality of the knowledge modeling by the knowledge base. The limited types of relations modeled by ontologies have caused most of the SRM methods to be able to detect and measure only a few special types of semantic relationships that is very far from the concept of semantic relatedness in human brain. Concepts of lexical resources are usually accompanied with a plain text narratively defines the concept. The information included in the definition of concepts sound very promising for SRM. This paper intends to treat this information as formal relations to improve SRM by distance-base methods. In order to do so, concepts glosses are mined for the semantic relations that are not modeled by the ontology. Then, these relations are employed in combination with classic relations of the ontology for semantic relatedness measurement according to the shortest path between concepts. Our evaluation demonstrated qualitative and quantitative improvement in detection of previously unknown semantic relationships and also stronger correlation with human judgment in SRM.
['Ehsan KhounSiavash', 'Kamran Zamanifar']
A new approach to use concepts definitions for semantic relatedness measurement