In the field of concepts, the measure of Wu and Palmer [1] has the advantage of being simple to implement and have good performances compared to the other similarity measures [2]. Nevertheless, the Wu and Palmer measure present the following disadvantage: in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. This situation is inadequate within the information retrieval framework. To overcome this problem, we propose a new similarity measure based on the Wu and Palmer measure. Our objective is to obtain realistic results for concepts not located in the same way. The obtained results show that compared to the Wu and Palmer approach, our measure presents a profit in terms of relevance and execution time.
[1] Z. Wu and M. Palmer. "Verb semantics and lexical selection". In
Proceedings of the 32nd Annual Meeting of the Associations for
Computational Linguistics, pp 133-138. 1994.
[2] D. Lin. "An Information-Theoretic Definition of similarity". In
Proceedings of the fifteenth International Conference on Machine
Learning (ICML'98). Morgan-Kaufmann: Madison, WI, pp.296-304.
1998.
[3] R. Baeza-Yates, B. Ribeiro-Neto. "Modern Information Retrieval". ACM
Press; Addison-Wesley: New York; Harlow, England; Reading, Mass.,
1999.
[4] G. Salton, M. J. McGill. "Introduction to modern information retrieval".
McGraw-Hill. New York, 1983.
[5] N.F. Noy and M. Musen. "PROMPT: Algorithm and Tool for
Automated Ontology Merging and Alignment". In Proceedings of
AAAI-2000, Austin, Texas. MIT Press/AAAI Press, 2000.
[6] M. Ehrig, S. Staab, Y. Sure. "Bootstrapping Ontology Alignment
Methods with APFEL". International Semantic Web Conference 2005.
pp. 186-200.
[7] P. Resnik (1995). "Using information content to evaluate semantic
similarity in taxonomy". In Proceedings of 14th International Joint
Conference on Artificial Intelligence, Montreal, 1995.
[8] N. Ho and F. Cédrick. "Lexical Similarity based on Quantity of
Information Exchanged-Synonym Extraction". In the Proceeding of
Conf. RIVF-04, February 2-5, 2004. Hanoi, Vietnam.
[9] J.H. Lee, M.H. Kim and Y.J. Lee. "Information Retrieval Based on
Conceptual Distance in IS-A Hierarchy". Journal of Documentation 49,
pp 188-207, 1993.
[10] R. Rada, H. Mili, E. Bichnell, and M. Blettner, "Development and
application of a metric on semantic nets". IEEE Transaction on Systems,
Man, and Cybernetics. pp 17-30. 1989.
[11] M.Ehrig, P.Haase, M.Hefke, and N.Stojanovic. "Similarity for ontologya
comprehensive framework". In Workshop Enterprise Modelling and
Ontology: Ingredients for Interoperability, 2004.
[12] J. Jiang et D. Conrath. « Semantic similarity based on corpus statistics
and lexical taxonomy". In Proceedings of International Conference on
Research in Computational Linguistics, Taiwan, 1997.
[13] C. Leacock and M. Chodorow. "Combining Local Context and WordNet
Similarity for Word Sense Identification. In WordNet": An Electronic
Lexical Database, C. Fellbaum, MIT Press, 1998.
[14] 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. pp. 95-130.
1999.
[15] T. Eiter, and H. Mannila. "Distance measures for point sets and their
computation". In Acta Informatica Journal, 34, 1997.
[16] J.Green, N.Horne, E.Orlowska and P. Siemens. "A Rough Set Model of
Information Retrieval". Theoretica Infomaticae 28, pp 273-296, 1996.
[17] R. C. Veltkamp, and L.J. Latecki. "Properties and Performances of
Shape Similarity Measures". 2006.
[18] M. Dean and G. Schreiber ed. "OWL Web Ontology Language
Reference. W3C Recommendation". 10 February 2004.
http://www.w3.org/TR/2004/REC-owl-ref-20040210/.
[19] G.Klyne and J.Carroll. "Web services description language (wsdl)1.1".
http://www.w3.org/TR/rdf-concepts/, 2004.
[20] A.Seaborne. "RDQL - A Query Language for RDF", W3C Member
Submission, 9 January 2004. http://www.w3.org/Submission/RDQL/.
[21] B.McBride. "Jena: Implementing the RDF Model and Syntax
Specification". In Proceedings of the Second International Workshop on
the Semantic Web. SemWeb'2001. May 2001.
[22] W. W. Cohen. "Data Integration Using Similarity Joins and a Word-
Based Information Representation Language". ACM Transactions on
Information Systems, Vol. 18, No. 3, July 2000.
[1] Z. Wu and M. Palmer. "Verb semantics and lexical selection". In
Proceedings of the 32nd Annual Meeting of the Associations for
Computational Linguistics, pp 133-138. 1994.
[2] D. Lin. "An Information-Theoretic Definition of similarity". In
Proceedings of the fifteenth International Conference on Machine
Learning (ICML'98). Morgan-Kaufmann: Madison, WI, pp.296-304.
1998.
[3] R. Baeza-Yates, B. Ribeiro-Neto. "Modern Information Retrieval". ACM
Press; Addison-Wesley: New York; Harlow, England; Reading, Mass.,
1999.
[4] G. Salton, M. J. McGill. "Introduction to modern information retrieval".
McGraw-Hill. New York, 1983.
[5] N.F. Noy and M. Musen. "PROMPT: Algorithm and Tool for
Automated Ontology Merging and Alignment". In Proceedings of
AAAI-2000, Austin, Texas. MIT Press/AAAI Press, 2000.
[6] M. Ehrig, S. Staab, Y. Sure. "Bootstrapping Ontology Alignment
Methods with APFEL". International Semantic Web Conference 2005.
pp. 186-200.
[7] P. Resnik (1995). "Using information content to evaluate semantic
similarity in taxonomy". In Proceedings of 14th International Joint
Conference on Artificial Intelligence, Montreal, 1995.
[8] N. Ho and F. Cédrick. "Lexical Similarity based on Quantity of
Information Exchanged-Synonym Extraction". In the Proceeding of
Conf. RIVF-04, February 2-5, 2004. Hanoi, Vietnam.
[9] J.H. Lee, M.H. Kim and Y.J. Lee. "Information Retrieval Based on
Conceptual Distance in IS-A Hierarchy". Journal of Documentation 49,
pp 188-207, 1993.
[10] R. Rada, H. Mili, E. Bichnell, and M. Blettner, "Development and
application of a metric on semantic nets". IEEE Transaction on Systems,
Man, and Cybernetics. pp 17-30. 1989.
[11] M.Ehrig, P.Haase, M.Hefke, and N.Stojanovic. "Similarity for ontologya
comprehensive framework". In Workshop Enterprise Modelling and
Ontology: Ingredients for Interoperability, 2004.
[12] J. Jiang et D. Conrath. « Semantic similarity based on corpus statistics
and lexical taxonomy". In Proceedings of International Conference on
Research in Computational Linguistics, Taiwan, 1997.
[13] C. Leacock and M. Chodorow. "Combining Local Context and WordNet
Similarity for Word Sense Identification. In WordNet": An Electronic
Lexical Database, C. Fellbaum, MIT Press, 1998.
[14] 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. pp. 95-130.
1999.
[15] T. Eiter, and H. Mannila. "Distance measures for point sets and their
computation". In Acta Informatica Journal, 34, 1997.
[16] J.Green, N.Horne, E.Orlowska and P. Siemens. "A Rough Set Model of
Information Retrieval". Theoretica Infomaticae 28, pp 273-296, 1996.
[17] R. C. Veltkamp, and L.J. Latecki. "Properties and Performances of
Shape Similarity Measures". 2006.
[18] M. Dean and G. Schreiber ed. "OWL Web Ontology Language
Reference. W3C Recommendation". 10 February 2004.
http://www.w3.org/TR/2004/REC-owl-ref-20040210/.
[19] G.Klyne and J.Carroll. "Web services description language (wsdl)1.1".
http://www.w3.org/TR/rdf-concepts/, 2004.
[20] A.Seaborne. "RDQL - A Query Language for RDF", W3C Member
Submission, 9 January 2004. http://www.w3.org/Submission/RDQL/.
[21] B.McBride. "Jena: Implementing the RDF Model and Syntax
Specification". In Proceedings of the Second International Workshop on
the Semantic Web. SemWeb'2001. May 2001.
[22] W. W. Cohen. "Data Integration Using Similarity Joins and a Word-
Based Information Representation Language". ACM Transactions on
Information Systems, Vol. 18, No. 3, July 2000.
@article{"International Journal of Information, Control and Computer Sciences:59438", author = "T. Slimani and B. Ben Yaghlane and K. Mellouli", title = "A New Similarity Measure Based On Edge Counting", abstract = "In the field of concepts, the measure of Wu and Palmer [1] has the advantage of being simple to implement and have good performances compared to the other similarity measures [2]. Nevertheless, the Wu and Palmer measure present the following disadvantage: in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. This situation is inadequate within the information retrieval framework. To overcome this problem, we propose a new similarity measure based on the Wu and Palmer measure. Our objective is to obtain realistic results for concepts not located in the same way. The obtained results show that compared to the Wu and Palmer approach, our measure presents a profit in terms of relevance and execution time.
", keywords = "Hierarchy, IS-A ontology, Semantic Web, Similarity
Measure.", volume = "2", number = "11", pages = "3871-5", }
