Abstract

In the context of ontology evolution in real world applications, particularly in the field of semantic web, ontologies are called to change in their structure as well as their semantic. It is necessary to evaluate the quality based on stability to make analysis to get appropriate enrichment manner for ontology evolution. In this paper, we introduce a new approach with that aims making three contributions. First, we present a new aspect of ontology quality based on its stability. Second, we present a new notion called ontology spectrum which can be used for analyzing ontology stability. Third, we provide an experimental method to evaluate this new aspect of quality within two processes: individual measure based on semantic similarity measures and global measure based on ontology spectrum.

References

• T. Berners-Lee, J. Hendler and O. Lassila. The Semantic Web, Scientific American 284:34–43, 2001.
• MA. Musen. Ontologies in biomedicine. AMIA 2008 tutorial T26. Washington, DC, November 9; 2008.
• AC. Yu. Methods in biomedical ontology. Journal Biomed Inform;39:252–66, 2006
• MA. Musen Scalable software architectures for decision support. Methods Inf Med; 38:229–38, 1999.
• DL Rubin, SE Lewis, CJ Mungall, S. Misra, Westerfield M Ashburner, et al. National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge. OMICS 2006; 10:185–98, 2006.
• A. Burton-Jones, VC. Storey, V. Sugumaran, P Ahluwalia. A semiotic metrics suite for assessing the quality of ontologies. In proceeding of Data and Knowledge Engineering 2005; 55(1): 84–102, 2005.
• A. Gangemi, C. Catenacci, M. Ciaramita, J. Lehmann, A theoretical framework for ontology evaluation and validation. In Proceedings of the Semantic Web Applications and Perspectives (SWAP), 2nd Italian Semantic Web Workshop, Trento, Italy, 2005.
• A.M. Orme, H. Yao, and L.H. Etzkorn, Indicating ontology data quality, stability, and completeness throughout ontology evolution, Journal of Software Maintenance, 49-75, 2007
• G. Beydoun, A.A. Lopez-Lorca, F.G. Sánchez, and R. Martínez-Béjar, "How do we measure and improve the quality of a hierarchical ontology?", Journal of Systems and Software, 2363-2373, 2011.
• J Brank., M Grobelnik., D Mladenic., A Survey of Ontology Evaluation Techniques, in Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD 2005), Ljubljana, Slovenia, 2005.
• J. Yu, J. Thom, A. Tam, Requirements-oriented methodology for evaluating ontologies, Information Systems 34:766–791, 2009
• J. Brank, D. Mladenic, M. Grobelnik, Gold standard based ontology evaluation using instance assignment, Proceedings of the 4th International Workshop on Evaluation of Ontologies for the Web (EON) at the 15th International World Wide Web Conference, Edinburgh, UK, 2006.
• A. Baneyx, J. Charlet, Evaluation, évolution et maintenance d’une ontologie en medecine : état des lieux et expérimentation. Revue I3 ; SI 2006 special issue on Ontological ressources, 2006
• R. Djedidi and M.A. Aufaure. Patrons de gestion des changements owl. In Fabien L. Gandon, editor. In proceedings of’knowledge engineering (IC), 145–156. PUG, 2009.
• S Tartir, IB Arpinar, M Moore, AP Sheth, B Aleman-Meza. OntoQA: Metric-based ontology quality analysis, In Proceedings of IEEE Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources, 2005.
• E. Blanchard, M. Harzallah, P. Kuntz and H. Briand. Sur l'évaluation de la quantité d'information d'un concept dans une taxonomie et la proposition de nouvelles mesures. Special issue "knowledge modeling" journal of new information technologies (RNIT). Cepadues (12), 127-145, 2008.
• Tversky, A. Features of similarity. Psychological Review 84(4), 327–352.
• P Jaccard, , Distribution of the alpine flora in the dranse’s basin and some neighbouring regions (in french). Bulletin de la Soc. Vaudoise Sci. Nat. (37), 241–272, 1901
• L. R. Dice, Measures of the amount of ecologic association between species. Ecology 26(3), 297–302, 1945.
• A.Ochiaï. Zoogeographic studies of the soleoid fishes found in japan and its neighbouring regions. Bulletin of the Japanese Society for Scientific Fisheries 22, 526–530. 1957.
• R. Rada, H. Mili, E. Bicknell, and M. Blettner. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19, Jan/Feb 1989.
• Z. Wu and M. Palmer. Verb semantics and lexical selection. In proceedings. of the 32nd annual meeting of the associations for Comp. Linguistics, 133–138. 1994
• P. H. Ganesan, Garcia-Molina and J. Widom. Exploiting hierarchical domain structure to compute similarity. ACM Trans. on Information Systems 21(1): 64–93, 2003.
• A. G. Maguitman, F. Menczer, H. Roinestad, and A. Vespignani. Algorithmic detection of semantic similarity. In proceedings of the 14th int. conf. on world wide web, 107–116. ACM Press, 2005
• P. Resnik, Semantic similarity in a taxonomy : An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11: 95–130. 1999
• J. J. Jiang, and D. W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. Proceeding of int. conf. on Research in Computational Linguistics, 19–33, 1997.
• N. Seco, T. Veale, and J. Hayes. An intrinsic information content metric for semantic similarity in wordnet. In proceedings of the 16th European conf. on artificial intelligence, 1089–1090, 2004.
• E. Blanchard, M. Harzallah and P. Kuntz. A generic framework for comparing semantic similarities on a subsumption hierarchy. In proceedings of 18th European Conference on Artificial Intelligence (ECAI),20-24, 2008.
• Ontology library of Protégé OWL : http://protege.stanford.edu/plugins/owl/owl-library/koala.owl.