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Inter Concept Ontology Similarity-Mapping of Feature Based Similarity to Information Theoretic Domain

K. Saruladha, Dr.G. Aghila, A. Bhuvaneswary

Abstract


Semantic similarity mechanism is mandatory in information retrieval, information integration, ontology mapping and psycholinguistics. Mapping from feature based model to information theoretic domain has been proposed to find the semantic closeness of concepts belonging to different biomedical ontologies. This project also aims in adapting existing intra ontology feature based approach (Pirró) and propose methods for assessing semantic similarity among concepts from inter ontologies. The possibility of extending Dice coefficient measure to inter concept ontology similarity is also attempted. The proposed idea was tested using MeSH and SNOMED-CT biomedical ontologies. The study of using Dice coefficient in information retrieval was also proposed. The proposed approach is corpus independent and it correlates well with the human judgements. The correctness of the proposed similarity metric is proved by comparing the result against the human judgements by computing the correlation coefficient. The proposed approaches outperform the other path length based computational methods as it achieves the highest correlation.

Keywords


Biomedical Domain, Semantic Similarity, Ontologies, inter Ontologies, MeSH, SNOMED-CT, Information Retrieval.

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References


K.Saruladha, G.Aghila, A.Bhuvaneswary, Computation of semantic similarity among cross ontological concepts based on Information Theoretic Approach, 2011.

T. Pedersen, S.V.S. Pakhomov, S. Patwardhan, C.G. Chute, “Measures of semantic similarity and relatedness in the biomedical domain”, Journal of Biomedical Informatics, vol. 40, no.3,pp. 288–299, 2007.

K.Saruladha, G.Aghila and A.Bhuvaneswary, “Computation of Semantic Similarity among Cross Ontological Concepts for Biomedical Domain”,Journal of Computing, vol 2, no.12, pp. 111-118, 2010.

A.Budanitsky and G. Hirst, “Evaluating WordNet-based measures of semantic distance”, Comput. Linguistics, vol. 32, no. 1, pp. 13–47, 2006.

H. A. Nguyen and H. Al-Mubaid, “Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies”, IEEE Trans. on Systems, Man, and Cybernetics,vol.39,no.4, pp. 339–398, 2009.

A.Tversky, “Features of similarity, Psychological Review”, vol. 84 no. 2, pp. 327– 352, 1977.

Rada, H. Mili, M. Bicknell, E. Blettner, “Development and application of a metric on semantic nets”, IEEE Trans. on Systems, Man, and Cybernetics vol. 19, pp. 17–30, 1989.

Claudia Leacock and Martin Chodorow, ”Combining local context and Word-Net similarity for word sense identification”, In Christiane Fellbaum, editor, WordNet: An Electronic Lexical Database, pp. 265–283. 1998.

P. Resnik, “Information content to evaluate semantic similarity in taxonomy”, Proc. of IJCAI, pp. 448–453, 1995.

D. Lin, “An information-theoretic definition of similarity”, in Proc. of Conference on Machine Learning, pp. 296–304, 1998.

J. Jiang, D. Conrath, ”Semantic similarity based on corpus statistics and lexical taxonomy”, Proc. of ROCLING X, 1997.

G. Pirró, N. Seco, “A new semantic similarity metric combining features and intrinsic information content”, in ODBASE, pp. 1271–1288, 2009.

N. Seco, T. Veale, J. Hayes, “An intrinsic information content metric for semantic similarity in WordNet”, in Proc. of ECAI, pp. 1089–1090, 2004.

M. Rodriguez, M. Egenhofer, “Determining semantic similarity among entity classes from different ontologies”, IEEE Trans. on Knowledge and Data Engineering, vol. 15, no. 2,pp. 442–456, 2003.

MeSH Browser (2010). Available: http://www.nlm.nih.gov/mesh/MBrowser.html

SNOMED-CT (2010). Available: http://www.snomed.org/index.html

Giuseppe Pirro and Jerome Euzenat, “A Feature and Information Theoretic Framework for Semantic Similarity and Relatedness”, 2010.

Angelos Hliaoutakis,, ”Semantic Similarity Measures in MeSH Ontology and their application to Information Retrieval on Medline”, 2005.


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