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A New Approach to Use Concepts Definitions for Semantic Relatedness Measurement

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AI 2011: Advances in Artificial Intelligence (AI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7106))

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Abstract

Semantic Relatedness Measurement (SRM) is one of the most important applications of reasoning by ontologies and different disciplines of AI, e.g. Information Retrieval, are firmly tied to it. The accuracy of SRM by lexical resources is largely determined by the quality of the knowledge modeling by the knowledge base. The limited types of relations modeled by ontologies have caused most of the SRM methods to be able to detect and measure only a few special types of semantic relationships that is very far from the concept of semantic relatedness in human brain. Concepts of lexical resources are usually accompanied with a plain text narratively defines the concept. The information included in the definition of concepts sound very promising for SRM. This paper intends to treat this information as formal relations to improve SRM by distance-base methods. In order to do so, concepts glosses are mined for the semantic relations that are not modeled by the ontology. Then, these relations are employed in combination with classic relations of the ontology for semantic relatedness measurement according to the shortest path between concepts. Our evaluation demonstrated qualitative and quantitative improvement in detection of previously unknown semantic relationships and also stronger correlation with human judgment in SRM.

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References

  1. Harabagiu, S., Moldovan, D.I.: Knowledge Processing on an Extended WordNet. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, vol. 305, pp. 381–405. MIT Press, Cambridge (1998)

    Google Scholar 

  2. Morris, J., Hirst, G.: Non-classical Lexical Semantic Relations. In: The HLT-NAACL Workshop on Computational Lexical Semantics, pp. 46–51. Association for Computational Linguistics, Stroudsburg (2004)

    Chapter  Google Scholar 

  3. Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Human Computer Studies 43, 907–928 (1995)

    Article  Google Scholar 

  4. Buitelaar, P., Cimiano, P., Magnini, B.: Ontology Learning from Text: An Overview. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications, vol. 123, pp. 3–12. IOS Press (2005)

    Google Scholar 

  5. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E.G.M., Milios, E.E.: Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web. In: 7th Annual ACM International Workshop on Web Information and Data Management, pp. 10–16. ACM Press, New York (2005)

    Google Scholar 

  6. Hirst, G., St-Onge, D.: Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, vol. 305, pp. 305–332. The MIT Press, Cambridge (1998)

    Google Scholar 

  7. Wu, Z., Palmer, M.: Verbs Semantics and Lexical Selection. In: 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics, Stroudsburg (1994)

    Chapter  Google Scholar 

  8. KhounSiavash, E., Baraani-Dastjerdi, A.: Using the Whole Structure of Ontology for Measuring Semantic Relatedness Measurement. In: 22th International Conference on Software Engineering and Knowledge Engineering, pp. 79–83. Knowledge Systems Institute Graduate School, IL, USA (2010)

    Google Scholar 

  9. KhounSiavash, E., Baraani-Dastjerdi, A.: Using the Density of Paths for Semantic Relatedness Measurement. In: 2010 Semantic Web and Web Services, pp. 18–24. CSREA Press, USA (2010)

    Google Scholar 

  10. Li, Y., Bandar, Z.A., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Transactions on Knowledge and Data Engineering 15, 871–882 (2003)

    Article  Google Scholar 

  11. Mazuel, L., Sabouret, N.: Semantic Relatedness Measure Using Object Properties in an Ontology. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 681–694. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Yang, D., Powers, D.M.W.: Measuring Semantic Similarity in the Taxonomy of WordNet. In: 28th Australasian Conference on Computer Science, vol. 38, pp. 315–322. Australian Computer Society, Inc., Newcastle (2005)

    Google Scholar 

  13. Rhee, S.K., Lee, J., Park, M.W.: Semantic Relevance Measure between Resources Based on a Graph Structure. In: International Multiconference on Computer Science and Information Technology, vol. 8, pp. 229–236. IEEE Press (2008)

    Google Scholar 

  14. Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: 14th International Joint Conference on Artificial Intelligence, Canada, Montreal, vol. 1, pp. 448–453 (1995)

    Google Scholar 

  15. Blanchard, E., Kuntz, P., Harzallah, M., Briand, H.: A Tree-based Similarity for Evaluating Concept Proximities in an Ontology. In: 10th Conference of the International Federation of Classification Societies, pp. 3–11. Springer, Heidelberg (2006)

    Google Scholar 

  16. Pirró, G.: A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data & Knowledge Engineering 68, 1289–1308 (2009)

    Article  Google Scholar 

  17. Lesk, M.: Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone. In: 5th Annual International Conference on Systems Documentation (SIGDOC 1986), pp. 24–26. ACM Press, New York (1986)

    Chapter  Google Scholar 

  18. Banerjee, S., Pedersen, T.: Extended Gloss Overlaps as a Measure of Semantic Relatedness. In: 18th International Joint Conference on Artificial Intelligence, vol. 18, pp. 805–810. Morgan Kaufmann Publishers Inc., San Francisco (2003)

    Google Scholar 

  19. Patwardhan, S., Pedersen, T.: Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In: EACL 2006 Workshop Making Sense of Sense: Bringing Computational Linguistics and Psycholinguistics Together, Italy, pp. 1–8 (2006)

    Google Scholar 

  20. Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: International Conference Research on Computational Linguistics (ROCLING X), Taiwan, pp. 19–33 (1997)

    Google Scholar 

  21. Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32, 13–47 (2006)

    Article  MATH  Google Scholar 

  22. Rada, M., Bicknell, E.: Ranking Documents with a Thesaurus. American Society for Information Science and Technology 40, 304–310 (1989)

    Article  Google Scholar 

  23. Alvarez, M.A., Lim, S.J.: A Graph Modeling of Semantic Similarity between Words. In: International Conference on Semantic Computing (ICSC), pp. 355–362. IEEE Press, Washington, DC (2007)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran

    Ehsan KhounSiavash & Kamran Zamanifar

Authors
  1. Ehsan KhounSiavash
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  2. Kamran Zamanifar
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Editor information

Editors and Affiliations

  1. School of Engineering and Mathematical Sciences, La Trobe University, 3086, Melbourne, VIC, Australia

    Dianhui Wang

  2. School of Computer Science and Software Engineering, The University of Western Australia, 6009, Perth, WA, Australia

    Mark Reynolds

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KhounSiavash, E., Zamanifar, K. (2011). A New Approach to Use Concepts Definitions for Semantic Relatedness Measurement. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_64

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  • DOI: https://doi.org/10.1007/978-3-642-25832-9_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25831-2

  • Online ISBN: 978-3-642-25832-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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