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GeT-based Ontology Construction for Semantic Disambiguation

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Published:04 December 2014Publication History

ABSTRACT

Semantic Ambiguation refers to the disagreement in meaning of terms used by parties in communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and related activities. Semantic Disambiguation can be performed through establishing and reconciling relative positions of parties in communication by matching their terms' meanings to a common domain-specific ontology -- a knowledge representation showing concepts, organized into domains, and relationships between them. In this paper, based on the wide existence and limitations of established hierarchical ontology in a form of Classification Schemes, we proposed a novel ontology structure combining the structured nature of hierarchy with expressive capability of graphs, called Graph-embedded Tree (GeT), and a novel approach to construct a GeT-based Ontology. Evaluation was performed on United States Patent Classification System (USPC); the results showed that the information retrieval backed by GeT-based ontology yields better disambiguation capability than other typical patent search methods.

References

  1. A. Halevy, "Why your data won't mix," Queue, vol. 8, no. 3, pp. 50--58, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. P. Sheth and J. A. Larson, "Federated database systems for managing distributed, heterogeneous and autonomous databases," ACM Computing Surveys (CSUR), vol. 3, no. 22, pp. 183--236, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. W. Kent, "The many forms of a single fact," in Thirty-fourth IEEE Computer Society International Conference: Intellectual Leverage, Digest of Papers, San Francisco, CA, USA, 1989.Google ScholarGoogle Scholar
  4. R. Hull, "Managing semantic heterogeneity in databases: a theoretical prospective," in Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGACT symposium on Principles of database systems, ACM, Tucson, AZ, USA, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. W. Bright, A. R. Hurson and S. Pakzad, "Automated resolution of semantic heterogeneity in multidatabases," ACM Transactions on Database Systems, vol. II, no. 19, pp. 212--253, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Garcia-Solaco, Manuel, F. Saltor and M. Castellanos, "Semantic heterogeneity in multidatabase systems," in Object-oriented multidatabase systems, UK, Prentice Hall International, 1995, pp. 129--202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. V. Ventrone, "Semantic heterogeneity as a result of domain evolution," ACM SIGMOD Record, vol. IV, no. 20, pp. 16--20, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. V. Kashyap and A. Sheth, "Semantic heterogeneity in global information systems," in Cooperative Infomration Systems: Current Trends & Directions, Academic Press, 1997.Google ScholarGoogle Scholar
  9. F. Hakimpour and A. Geppert, "Resolving semantic heterogeneity in schema integration," in Proceedings of the international conference on Formal Ontology in Information Systems-Volume 2001, Ogunquit, ME, USA, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Bhattacharjee and S. K. Ghosh, "Automatic Resolution of Semantic Heterogeneity in GIS: An Ontology Based Approach," in Advancec Computing, Networking and Informatics, 2014.Google ScholarGoogle Scholar
  11. L. Ying, Z. Huimin, L. Hui and Z. Chengli, "Ontology-Based Knowledge Representation for Resolution of Semantic Heterogeneity in GIS," in International Conference on Management of e-Commerce and e-Government, London, UK, 2013.Google ScholarGoogle Scholar
  12. Google, "Google Search Engine," Google, {Online}. Available: www.google.com. {Accessed 20 June 2014}.Google ScholarGoogle Scholar
  13. Microsoft, "Bing Search Engine," Microsoft, {Online}. Available: www.bing.com. {Accessed 20 June 2014}.Google ScholarGoogle Scholar
  14. Yahoo, "Yahoo Search Engine," Yahoo, {Online}. Available: search.yahoo.com. {Accessed 20 June 2014}.Google ScholarGoogle Scholar
  15. United States Patent and Trademark Office, "United States Patent Classification System," 16 April 2013. {Online}. Available: http://www.uspto.gov/web/patents/classification/index.htm. {Accessed 12 June 2014}.Google ScholarGoogle Scholar
  16. USPTO and EPO, "Cooperative Patent Classification," 1 June 2014. {Online}. Available: http://www.cooperativepatentclassification.org/index.html;jsessionid=kqg6guyifk32. {Accessed 19 June 2014}.Google ScholarGoogle Scholar
  17. World Intellectual Property Organization, "International Patent Classification," {Online}. Available: http://www.wipo.int/classifications/ipc/en/. {Accessed 18 June 2014}.Google ScholarGoogle Scholar
  18. Association for Computing Machinery, "The 2012 ACM Computing Classification System," {Online}. Available: http://www.acm.org/about/class/class/2012. {Accessed 17 June 2014}.Google ScholarGoogle Scholar
  19. Stanford Center for Biomedical Informatics Research, "Protege," 2014. {Online}. Available: http://protege.stanford.edu/. {Accessed 13 May 2014}.Google ScholarGoogle Scholar
  20. A. Farquhar, R. Fikes and J. Rice, "The ontolingua server: A tool for collaborative ontology construction," International journal of human-computer studies, vol. VI, no. 46, pp. 707--727, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. McGuinness, D. L., R. Fikes, J. Rice and S. Wilder, "The chimeara ontology environment," in Proceedings of Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, Austin, Texas, USA, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Missikoff and F. Taglino, "Symontox: a web-ontology tool for ebusiness domains," in Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. Maedche and S. Staab, "Ontology learning for the semantic web," IEEE Intelligent systems, vol. II, no. 16, pp. 72--79, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. H. Bohring and S. Auer, "Mapping XML to OWL Ontologies," Leipziger Informatik-Tage, no. 72, pp. 147--156, 2005.Google ScholarGoogle Scholar
  25. D. Gasevic, D. Djuric, V. Devedzic and V. Damjanovic, "From UML to ready-to-use OWL ontologies," in Proceedings of 2nd International IEEE Conference on Intelligent Systems, 2004.Google ScholarGoogle Scholar
  26. B. Biebow, S. Szulman and A. J. Clement, "TERMINAE: A linguistics-based tool for building of a domain ontology," in Knowledge Acquisition, Modeling and Management, Springer Berlin Heidelberg, 1999, pp. 49--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. J. Nobecourt, "A method to build formal ontologies from texts," EKAW-2000, Juan-Les-Pins, 2000.Google ScholarGoogle Scholar
  28. D. Lonsdale, Y. Ding, D. W. Embley and A. Melby, "Peppering knowledge sources with SALT: Boosting conceptual content for ontology generation," in Proceeding of the AAAI Workshop: Semantic Web Meets Language Resources, 2002.Google ScholarGoogle Scholar
  29. H. Hu and D.-Y. Liu, "Learning OWL ontologies from free texts," in Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004.Google ScholarGoogle Scholar
  30. L. Khan and F. Luo, "Ontology Construction for Information Selection," in Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, Washington, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Princeton University, "About WordNet," 7 November 2013. {Online}. Available: http://wordnet.princeton.edu/. {Accessed 12 May 2014}.Google ScholarGoogle Scholar
  32. H. Kong, M. Hwang and P. Kim, "Design of the automatic ontology building system about specific domain knowledge," in Proceedings of the 8th International Conference on Advanced Communication Technology, 2006.Google ScholarGoogle Scholar
  33. D. I. Moldovan and R. Girju, "Domain-specific knowledge acquisition and classification using wordnet," in Proceedings of 13th International Florida Artificial Intelligence Research Society Conference, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. E. Agirre, O. Ansa, E. Hovy and D. Martinez, "Enriching Very Large Ontologies Using WWW," in Proceedings of Ontology Learning Workshop, Berlin, 2000.Google ScholarGoogle Scholar
  35. M. Cho, H. Kim and P. Kim, "A new method for ontology merging based on concept using wordnet," in Proceedings of 8th International Conference on Advanced Communication Technology, 2006.Google ScholarGoogle Scholar
  36. J. Kietz, A. Maedche and R. Volz, "A method for semi-automatic ontology acquisition from a corporate intranet," in Proceedings of EKAW-2000 Workship Ontologies and Texts, Juan-Les-Pins, 2000.Google ScholarGoogle Scholar
  37. S. B. Navathe and R. Elmasri., Fundamentals of database systems, Pearson Education, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services
          December 2014
          587 pages

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          • Published: 4 December 2014

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