An approach to merge domain ontologies using granular computing

Granular computing is the emerging technique which performs data processing through making multiple levels of descriptions. Each level of description is expressed through granules or chunks of data also defined as information granules. The granule, the granule structure, and the granule layer are the heart of granular computing. Ontologies are vital information archives. On all disciplines of science and technology, ontologies are developed according to the requirements. Hence, the huge number of ontologies is available in the concerned domain which creates information duplication and storage problem. Merging of existing ontologies overcomes these issues. There are many merging approaches available. The existing approaches do not use granular computing for merging the ontologies. The proposed approach employs granular computing for merging the existing domain ontologies, thereby unifying multiple domain ontologies into a single representative domain ontology. For that, this research work proposes the following four granular computing processes, namely, association, isolation, purification, and reduction which can be applied over a group of similar nodes in the ontologies thereby unifying them. The proposed method achieves the ontology merging by performing two phases, namely similarity calculation phase and granular computing phase. The similarity calculation phase identifies the inter-label similarity between the labels of ontologies and computes the relevant group of nodes. Subsequently, granular computing applies association, isolation, purification, and reduction over a group of relevant nodes. The proposed approach is validated using the film industry and transportation domain ontologies and compared against its counterpart hybrid semantic similarity measure (HSSM). The results concluded that the proposed approach outperforms HSSM.