IJMTES – AN EFFICIENT APPROACH FOR RANKING ONTOLOGIES BASED ON THE COMBINATION OF CONTENT BASED AND SEMANTIC MATCHING

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Surumi Haris, Prof R.Subhashini

Volume 01 Issue o5  Year 2014  

ISSN no:  2348-3121

Page no: 236-239

Abstract—The semantic web can be considered as a semantic expansion for the web and the ontologies act as the backbone of semantic web. Over these years an increasing number of ontologies have been developed. The development of new ontologies does not tap the full potential of existing knowledge sources and ongoing ontology engineering methodologies do not address ontology reuse to a satisfactory extent yet. Selecting the desired ontology from existing ontologies is essential for ontology reuse. Several works has been done for ontology selection. However, in these cases, it is almost impossible to find an ontology that includes all the concepts matched by the search terms at the semantic level. To deal with this, the proposed work uses combination of Content Based Ontology Ranking and Ontology Rank model consisting of selection standards and metrics based on better semantic matching capabilities. The model proposed presents two novel features different from previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure. Experimental result provides better result when compare with the existing ranking system.

Keywords—Ontology; Ontology Ranking; Semantic similarity, Relation matching; Taxonomy matching; Ranking Techniques

Reference

[1] Jinsoo Park, Sunjoo Oh, Joongho Ahn, “Ontology Selection Ranking Model for Knowledge Reuse,” Expert Systems with Applications 38 (2011) 5133–5144.
[2] Alani, H., & Brewster, C. “Ontology ranking based on the analysis of concept Structures,” In Proceedings of the third international conference on knowledge capture (K-CAP’05), Banff, Canada (2005).
[3] Buitelaar P., Eigner T., & Declerck T., “OntoSelect: A dynamic ontology library with support for ontology selection,” In Proceedings of the demo session at the international Semantic Web conference, Hiroshima, Japan. (2004).
[4] Vandana Dhingra, “Comparative Analysis of Ontology Ranking Algorithms,” International Journal of Information Technology and Web Engineering, 7(3), 55-66, July -September (2012).
[5] Ding. L., Finin. T., Joshi. A., Peng. Y., Cost R. S., & Sachs J, et al. “Swoogle: A Search and metadata engine for Semantic Web,” In Proceedings of the thirteenth ACM conference on information and knowledge management, (2004).
[6] Ding L, et al. “Finding and Ranking Knowledge on the Semantic Web,” Proceedings of the 4th International Semantic Web Conference 156-170107
[7] Harith Alani, Christopher Brewster and Nigel Shadbolt, “Ranking Ontologies with AKTive Rank,” (2007).
[8] Matthew Jones and Harith Alani, “Content-based Ontology Ranking,” 9th International Protégé Conference, July (2006).
[9] Samir Tartir and I. Budak Arpinar, “Ontology Evaluation and Ranking using OntoQA,” (2007).
[10] Zhiguo Ding and Zhengjie Duan, “Improved Ontology Ranking Algorithm Based on Semantic Web,” 978-1-4244-6709-9/10/$26.00 ©2010 IEEE
[11] R. Subhashini, J. Akilandeswari, V. Sindhuja, “A Review on ontology Ranking Algorithms,” International Journal of Computer Applications (0975- 8887), November (2011).
[12] Harith Alani. “Position paper: Ontology construction from online ontologies,” WWW2006, Edinburgh, UK, May 22–26, (2006).
[13] Janez Brank, Marko Grobelnik, Dunja Mladenic, “A survey on ontology evaluation techniques.”
[14] Jeff Z. Pan, Edward Thomas and Derek Sleeman, “Ontosearch2: Searching And Querying Web Ontologies.”

Full Pdf Paper-Click Here

 

Scroll Up