ONTOLOGY VALIDATION ALGORITHM ON DATA DRIVEN APPROACH AND VOCABULARY ASPECT

Authors

  • Radziah Mohamad Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Nurhamizah Mohd-Hamka Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6184

Keywords:

Data driven, ontology evaluation, similarity, coverage

Abstract

Ontology evaluation is required before using the ontology within applications. Similar with software practice, the purpose of ontology evaluation is to identify the achievement of requirement criteria.  Users who require coverage criteria often seeking ontology that contain the terms related to their focused domain knowledge. Users encounter the difficulty to select a suitable ontology from variety of ontology evaluation approaches. Conceptualization of information related to ontology evaluation helps to identify the important component within ontology that helps towards coverage criteria achievement. This work proposes an algorithm to extract ontology documents gained from public ontology repositories like Falcons into its vocabulary parts focused on classes and literals. The algorithm then processes the extracted ontology components with similarity algorithm and later displays the result on the coverage match of ontology with provided terms and the terms that are synonym expanded using WordNet. 

References

Brewster, C., Alani, H., Dasmahapatra, S., and Wilks, Y. 2004. Data Driven Ontology Evaluation. In Proceedings of International Conference on Language Resources and Evaluation. 2004: 164-169.

Abdullah, N. and Ibrahim, R. 2012. Knowledge Retrieval using Hybrid Semantic Web Search. In 2012 International Conference on Computer & Information Science (ICCIS). 61-65.

Netzer, Y., Gabay, D., Adler, M., Goldberg, Y., and Elhadad, M. 2009. Ontology Evaluation through Text Classification. In Advances in Web and Network Technologies, and Information Management. vol. 5731, Chen L., Liu C., Zhang X., Wang S., Strasunskas D., Tomassen S. L., Rao J., Li W.-S., Candan K. S., Chiu D. K. W., Zhuang Y., Ellis C. A., and Kim K.-H., Eds. Springer Berlin Heidelberg. 210-221.

Noy, N. F., Alexander, P. R., Harpaz, R., Whetzel, P. L., Fergerson, R. W., and Musen, M. A. 2013. Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking. In International Semantic Web Conference 1. volume 8218 of Lecture Notes in Computer Science. 444-459.

VrandeÄić, D. 2010. Ontology Evaluation. Springer.

White, S. 1992. How to Strike a Match.[Online]. Available: http://www.catalysoft.com/articles/strikeamatch.html. [Accessed: 03-Aug-2014].

Yao, L., Divoli, A., Mayzus, I., Evans, J. a, and Rzhetsky, A. 2011. Benchmarking Ontologies: Bigger or Better? PLoS Comput. Biol. 7(1): 1-15.

Jakulin, A. and Mladenić, D. 2005. Ontology Grounding. In Proceedings of 8th Inter- national Multi-Conference Information Society IS-2005. 170-173.

Bouiadjra, A. B. and Benslimane, S.-M. 2011. FOEval: Full Ontology Evaluation. In 2011 7th International Conference on Natural Language Processing and Knowledge Engineering. 464-468.

Pivovarov, R. and Elhadad, N. 2012. A Hybrid Knowledge-based and Data-Driven Approach to Identifying Semantically Similar Concepts. J. Biomed. Inform. 45(3): 471-481.

Yildiz, B. and Miksch, S. 2007. Ontox-A Method for Ontology-driven Information Extraction. Comput. Sci. Its Appl. 4707: 1-14.

Sembok, T. M. T., Bakar, Z. A., and Ahmad, F. 2011. Experiments in Malay Information Retrieval. Proc. 2011 Int. Conf. Electr. Eng. Informatics, ICEEI 2011.

Zavitsanos, E., Paliouras, G., and Vouros, G. A. 2011. Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment. Knowl. Creat. Diffus. Util. 23(11): 1635-1648.

Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. [Online]. Available: https://wordnet.princeton.edu/. [Accessed: 03-Oct-2014].

Mohd-Hamka N. and Mohamad R. 2014. OntoUji – Ontology to Evaluate Domain Ontology for Semantic Web Services Description. J. Teknol. 6(Special Issue on Current and Emerging Trends in Technology, Science and Engineering): 3: 21-26.

Euzenat, J. and Shvaiko, P. 2007.Ontology Matching.

Mohamad, R. and Mohd-Hamka, N. 2014. Similarity Algorithm for Evaluating the Coverage of Domain Ontology for Semantic Web Services. In 2014 8th Malaysian Software Engineering Conference (MySEC). 189-194.

Downloads

Published

2015-11-09

Issue

Section

Science and Engineering

How to Cite

ONTOLOGY VALIDATION ALGORITHM ON DATA DRIVEN APPROACH AND VOCABULARY ASPECT. (2015). Jurnal Teknologi, 77(9). https://doi.org/10.11113/jt.v77.6184