Extended TvX: A New Method Feature Based Semantic Similarity for Multiple Ontology


  • Nurul Aswa Omar Department Web Technology, Faculty Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia.
  • Shahreen Kasim Soft Computing And Data Mining Center, Faculty Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia.
  • Mohd Farhan Md Fudzee Department Multimedia, Faculty Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia.


Semantic Similarity, Feature Based, Ontology, Multiple Ontology, Cross Ontology, Heterogeneous Sources,


Semantic similarity between the terms is the main phase in information retrieval and information integration, which requires semantic content matching. Semantic similarity function is important in psychology, artificial intelligence and cognitive science. The problem of integrating various sources is the matching between ontological concepts. In this paper, we proposed to develop this method by analyzing the semantic similarity between the modeled taxonomical knowledge and features in different ontology. This paper contains a review on semantic similarity and multiple ontology that focuses on the feature-based approach. Besides that, we proposed a method, namely a semantic similarity that overcomes the limitation of different features of terms compared. As a result, we are able to develop a better method that improves the accuracy of the similarity measurement.


Doan A., Madhavan J., Domingos P., and Halevy A., 2004. Ontology matching: A Machine Learning Approach. Handbook On Ontologies. 1–20.

Batet M., Sánchez D., Valls A., and Gibert K., 2013. Semantic similarity Estimation From Multiple Ontologies, Applied Intelligence. 38(1):29–44.

Sánchez D.and Isern D., 2011. Automatic Extraction Of Acronym Definitions From Theweb. Applied Intelligence. 34: 311–327.

Sánchez D., Isern D., and Millan M., 2011. Content Annotation For The Semantic Web: An Automatic Web-Based Approach. Knowledge and Information Systems. 27:393–418.

Iannone L., Palmisano I., and Fanizzi N., 2007. An Algorithm Based On Counterfactuals For Concept Learning In The Semantic Web. Applied Intelligence. 26:139–159.

Saruladha K., Aghila G., and Bhuvaneswary A., 2011. COSS: Cross Ontology Semantic Similarity measure — An Information Content Based Approach. 2011 International Conference on Recent Trends in Information Technology (ICRTIT). 485–490.

Al-Mubaid H.and Nguyen H., 2009. Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews. 39(4):389–398,

Budanitsky A. andHirst G., 2006. Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics. 2:13–47.

a Hliaoutakis, Varelas G., Voutsakis E., Petrakis E. G. M., and Milios E., 2006. Information Retrieval by Semantic Similarity. International Journal on Semantic Web and Information Systems. 2:55–73.

Pirrò G., Ruffolo M., and Talia D., 2009. SECCO: On Building Semantic Links In Peer-To-Peer Networks. In Lecture Notes In Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5480 LNCS. 1–36.

Petrakis E., Varelas G., Hliaoutakis A., and Raftopoulou P., XSimilarity: Computing Semantic Similarity between Concepts from Different Ontologies. Journal of Digital Information Management. 4(4):233, 2006.

Elavarasi S., Akilandeswari J., and Menaga K., 2014. A Survey on Semantic Similarity Measure. ijrat.org. 2 (3):389–398.

Rada R., Mili H., Bicknell E., and Blettner M., 1989. Development And Application Of A Metric On Semantic Nets. IEEE Transactions on Systems, Man, and Cybernetics. 19(1):17–30.

Sánchez D., Batet M., Isern D., and Valls A., 2012. Ontology-Based Semantic Similarity: A New Feature-Based Approach, Expert Systems with Applications. 39:7718–7728,

Rodríguez M. and Egenhofer M., 2003. Determining Semantic Similarity Among Entity Classes From Different Ontologies, Knowledge and Data. 15(2)442–456.

Sánchez D. and Batet M., 2013. A Semantic Similarity Method Based On Information Content Exploiting Multiple Ontologies, Expert Systems with Applications. 40(4): 1393–1399, Mar.

Li H., Tian Y., and Cai Q., 2011. Improvement Of Semantic Similarity Algorithm Based on WordNet. in Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011. 564–567.

Tversky A., 1977. Features of Similarity. Psychological Review. 84:327–352,

Sánchez D., Solé-Ribalta A., Batet M., and Serratosa F., 2012. Enabling Semantic Similarity Estimation Across Multiple Ontologies: An Evaluation In The Biomedical Domain., Journal Of Biomedical Informatics. 45(1):141–55, Feb.



Kasim S., Deris S., Othman R. M. 2013. Multi-Stage Filtering For Improving Confidence Level And Determining Dominant Clusters In Clustering Algorithms Of Gene Expression Data. Computers In Biology And Medicine. 43(9):1120-1133.




How to Cite

Omar, N. A., Kasim, S., & Md Fudzee, M. F. (2017). Extended TvX: A New Method Feature Based Semantic Similarity for Multiple Ontology. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-3), 39–43. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2270