Ontology Partitioning: Clustering Based Approach

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Soraya Setti Ahmed 1,* Mimoun Malki 1 Sidi Mohamed Benslimane 2

1. EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, 22000, Algeria

2. High School of Computer Science (ESI- of Sidi Bel Abbes), Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.06.01

Received: 18 Dec. 2014 / Revised: 4 Feb. 2015 / Accepted: 11 Mar. 2015 / Published: 8 May 2015

Index Terms

Ontology, Partition Algorithm, Modularization, Ontology Owl, K-Means Clustering Algorithm, Similarity Measures


The semantic web goal is to share and integrate data across different domains and organizations. The knowledge representations of semantic data are made possible by ontology. As the usage of semantic web increases, construction of the semantic web ontologies is also increased. Moreover, due to the monolithic nature of the ontology various semantic web operations like query answering, data sharing, data matching, data reuse and data integration become more complicated as the size of ontology increases. Partitioning the ontology is the key solution to handle this scalability issue. In this work, we propose a revision and an enhancement of K-means clustering algorithm based on a new semantic similarity measure for partitioning given ontology into high quality modules. The results show that our approach produces meaningful clusters than the traditional algorithm of K-means.

Cite This Paper

Soraya Setti Ahmed, Mimoun Malki, Sidi Mohamed Benslimane, "Ontology Partitioning: Clustering Based Approach", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.6, pp.1-11, 2015. DOI:10.5815/ijitcs.2015.06.01


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