SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching
Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on large-scale matching problems. To this end, in this paper, we come up with a new partitioning-based matching approach, where a new clustering method for partitioning concepts of ontologies is introduced. The proposed method, called SeeCOnt, is a seeding-based clustering technique aiming to reduce the complexity of comparison by only using clusters’ seed. In particular, SeeCOnt first identifies and determines the seeds of clusters based on the highest ranked concepts using a distribution condition, then the remaining concepts are placed into the proper cluster by defining and utilizing a membership function. The SeeCOnt method can improve the memory consuming problem in the large-scale matching problem, as well as it increases the matching quality. The experimental evaluation shows that SeeCOnt, compared with the top ten participant systems in OAEI, demonstrates acceptable results.
KeywordsOntology matching Clustering techniques Large-scale matching
A. Algergawy’work is partly funded by DFG in the INFRA1 project of CRC AquaDiva.
- 5.Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AAAI AI Mag. 25(1), 83–94 (2005)Google Scholar
- 6.Doan, A., Halevy, A.Y., Ives, Z.G.: Principles of Data Integration. Morgan Kaufmann, USA (2012) Google Scholar
- 11.Graves, A., Adali, S., Hendler, J.: A method to rank nodes in an RDF graph. In: 7th International Semantic Web Conference (Posters and Demos) (2008)Google Scholar
- 18.Rahm, E.: Towards large-scale schema and ontology matching. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Data-Centric Systems and Applications, vol. 5258, pp. 3–27. Springer, Heidelberg (2011)Google Scholar
- 22.Shvaiko, P., Euzenat, J., Mao, M., Jimnez-Ruiz, E., Li, J., Ngonga, A.: editors. 9th International Workshop on Ontology Matching collocated with the 13th International Semantic Web Conference (ISWC 2014) (2014)Google Scholar
- 25.Zhong, Q., Li, H., Li, J., Xie, G.T., Tang, J., Zhou, L., Pan, Y.: A Gauss function based approach for unbalanced ontology matching. In: the ACM SIGMOD International Conference on Management of Data, (SIGMOD 2009), pp. 669–680 (2009)Google Scholar