The similarity measurement of mapping between ontologies has been evaluated through prior terms like instance, properties and association. Concept similarity measurement (CSM) technique is most applicable for mapping among ontologies which calculates similarity matching of data given by user (i.e. data mention in search query, mostly various keywords are typed). The basic purpose of this research is to suggest a SM technique which uses similar super concepts for mapping and increase the matching similarity with some additional datasets. We have analyzed the various limitations in previous techniques and have effort to modify the limitations of these techniques in our proposed architecture. The paper presents a layered approach for similarity matching using super concepts and measures the synonym of concepts by using domain vocabulary. It also uses the (ESR) explicit semantic relation to measure the responsibility of concepts which create conflict in mapping process. A sample case study has employed to test the SM technique of proposed architecture .Two matrices for evaluation which is efficiency and definitiveness parameters has used to improve performance of mapping by adding concept explicit semantic relation. The proposed architecture provides the enhancement of mapping results by classify super concepts which are based on the semantic responsibility of concepts. This improvement plays a vital role in the domain of ontologies for mapping, alignment and merging and will also give a clear, obvious outcome of search.
Farah Shahid, Maruf Pasha (2017); A Layered Approach to Inferring Similarity Measurement of Ontologies Using Concept Mapping;
Int J Sci Res Publ 7(4) (ISSN: 2250-3153). http://www.ijsrp.org/research-paper-0417.php?rp=P646317