Semantic Similarity Measure Based on Concreteness Degree of a Concept

Article Preview

Abstract:

Semantic similarity between concepts is widely used, but the measuring method is still a challenging task. We proposed a semantic similarity measuring method, using concreteness degree of a concept which is based on constructing process of ontology. Firstly, Concreteness degree of concept was defined for the concept by depth of the concept itself and its most specific descendant, then according to the Jaccards Coefficient the semantic similarity between concepts measured by the specified process of the two compared concepts and their co-specified process. Experiment result shows that the proposed method gained a higher correlation coefficient to human judgments than other compared measures.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

852-856

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Atkinson J, Ferreira A, Aravena E. Discovering implicit intention-level knowledge from natural-language texts. Knowledge-Based Systems, 2009, 22(7): 502-508.

DOI: 10.1016/j.knosys.2008.10.007

Google Scholar

[2] Liu Yu-Peng, Li Sheng, Zhao Tie-Jun. System combination based on WSD using WordNet. Acta Automatica Sinica, 2010, 36(11): 1575-1580.

DOI: 10.3724/sp.j.1004.2010.01575

Google Scholar

[3] Sánchez D D, Isern D, Millan M. Content annotation for the semantic web: an automatic web-based approach. Knowledge and Information Systems, 2011, 27(3): 393-418.

DOI: 10.1007/s10115-010-0302-3

Google Scholar

[4] Sánchez D D. A methodology to learn ontological attributes from the web. Data and Knowledge Engineering, 2010, 69(6): 573-597.

DOI: 10.1016/j.datak.2010.01.006

Google Scholar

[5] Bai Dong-Wei. Research on Web Services Semantic Matchmaking and Discovery [Ph. D. dissertation], Beijing University of Posts and Telecommunication, China, (2007).

Google Scholar

[6] Qiu Tian, Li Peng-Fei, Lin Pin. A web service matching algorithm based on semantic similarity of concepts. Acta Electronica Sinica, 2009, 37(2): 429-432.

Google Scholar

[7] Sánchez D, Batet M, Valls A, Gibert K. Ontology-driven web-based semantic similarity. Journal of Intelligent Information Systems, 2010, 35(3): 383-413.

DOI: 10.1007/s10844-009-0103-x

Google Scholar

[8] Lemaire B, Denhiere G. Effects of high-order co-occurrences on word semantic similarities [Online], available: http: /cpl. revues. org/document471. html, December 9, (2011).

Google Scholar

[9] Turney P D. Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the twelfth European conference on machine learning. Freiburg, Germany, 2001, p.491–499.

DOI: 10.1007/3-540-44795-4_42

Google Scholar

[10] Downey D, Broadhead M and Etzioni O. Locating complex named entities in Web text. In Proceedings of the 20th international joint conference on artificial intelligence. 2007, p.2733–2739.

Google Scholar

[11] Tversky A. Features of similarity[J]. Psychological Review, 1977, 84(4): 327-352.

Google Scholar

[12] Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets[J]. IEEE Transaction on Systems, Man and Cybernetics, 1989, 19(1): 17-30.

DOI: 10.1109/21.24528

Google Scholar

[13] Wu Z B, Palmer M. Verb semantics and lexical selection[C]. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico, USA, 1994. 133-138.

DOI: 10.3115/981732.981751

Google Scholar

[14] Leacock C, Chodorow M. Combining local context and WordNet similarity for word sense identification[S]. In WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press, 1998. 265-283.

DOI: 10.7551/mitpress/7287.003.0018

Google Scholar

[15] Bollegala D, Matsuo Y, Ishizuka M. Measuring semantic similarity between words using web search engines. In: Proceedings of the 16th International Conference on World Wide Web. Banff, Canada: ACM, 2007: 757-766.

DOI: 10.1145/1242572.1242675

Google Scholar

[16] Resnik P. Using information content to evaluate semantic similarity in a taxonomy[C]. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. Montreal, Quebec, Canada: Morgan Kaufmann, 1995: 448-453.

Google Scholar

[17] Seco N, Veale T, Hayes J. An intrinsic information content metric for semantic similarity in WordNet[C]. In: Proceedings of 16th European Conference on Artificial Intelligence, ECAI 2004, including Prestigious Applicants of Intelligent Systems. Valencia, Spain: IOS Press, 2004: 1089-1090.

Google Scholar

[18] Lin D. An information-theoretic definition of similarity[C]. In: Proceedings of the 15th international conference on machine learning. San Francisco, USA: Morgan Kaufmann, 1998: 296-304.

Google Scholar

[19] Rubenstein H, Goodenough J. Contextual correlates of synonymy[J]. Commun. ACM, 1965, 8(10): 627-633.

DOI: 10.1145/365628.365657

Google Scholar

[20] Miller G A, Charles W G. Contextual correlates of semantic similarity[J]. Lang. Cogn. Process, 1991, 6(1): 1-28.

Google Scholar

[21] Li Wenqing. Research on Key Techniques of Distributed Web Service Discovery System [Ph. D. dissertation], Beijing Institution of Technology, China, (2012).

Google Scholar