Ontology Partitioning: Clustering Based Approach

The semantic web goal is to share and integrate data\udacross different domains and organizations. The knowledge\udrepresentations of semantic data are made possible by ontology.\udAs the usage of semantic web increases, construction of the\udsemantic web ontologies is also increased. Moreover, due to\udthe monolithic nature of the ontology various semantic web\udoperations like query answering, data sharing, data matching,\uddata reuse and data integration become more complicated as the\udsize of ontology increases. Partitioning the ontology is the key\udsolution to handle this scalability issue. In this work, we propose\uda revision and an enhancement of K-means clustering algorithm\udbased on a new semantic similarity measure for partitioning given\udontology into high quality modules. The results show that our\udapproach produces meaningful clusters than the traditional\udalgorithm of K-means