JACIII Vol.10 No.5 pp. 733-737
doi: 10.20965/jaciii.2006.p0733


Efficient Merging for Heterogeneous Domain Ontologies Based on WordNet

Hyunjang Kong, Myunggwon Hwang, and Pankoo Kim

Department of Computer Science & Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea

October 28, 2005
March 17, 2006
September 20, 2006
ontology merging, WordNet
As semantic web study progresses, domain ontologies built by engineers are based on writer’s academic background and research interests, preventing a uniform or standardized ontology using standard ontology building tools and ontology languages and restricting interoperability. Current ontology building tools focus on creating, editing and inferencing ontology efficiently but do not offer a way for merging heterogeneous domain ontologies. This paper presents a way for merging heterogeneous ontologies efficiently and correctly.
Cite this article as:
H. Kong, M. Hwang, and P. Kim, “Efficient Merging for Heterogeneous Domain Ontologies Based on WordNet,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.5, pp. 733-737, 2006.
Data files:
  1. [1] D. Calvanese, D. G. Giuseppe, and M. Lenzerini, “Ontology of Integration and Integration of Ontologies,” In Proceedings of the 2001 Description Logic Workshops (DL 2001), 2001.
  2. [2] A. Maedche, “A Machine Learning Perspective for the Semantic Web,” Semantic Web Working Symposium (SWWS) Position paper, 2001.
  3. [3] T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web,” Scientific Am., Vol.284, No.5, pp. 34-43, May, 2001.
  4. [4] P. Mitra, G. Wideerhold, and J. Jannink, “Semi-automatic Integration of Knowledge Sources,” In Proceedings of Fusion’99, 1999.
  5. [5] T. Milo and S. Zohar, “Using schema matching to simplify heterogeneous data translation,” In proceedings of the International Conference on Very Large Databases (VLDB), 1998.
  6. [6] N. F. Noy and M. A. Musen, “PROMPT:Algorithm and Tool for Automated Ontology Merging and Alignment,” In proceedings of the National Conference on Artificial Intelligence (AAAI), 2000.
  7. [7] N. F. Noy and M. A. Musen, “Anchor-PROMPT:Using Non-Local Context for Semantic Matching,” In proceedings of the Workshop on Ontologies and Information Sharing at the International Joint conference on Artificial Intelligence (IJCAI), 2001.
  8. [8] V. Rijsbergen, “Information Retrieval,” London: Butterworths, 1979, Second Edition.
  9. [9] R. Agrawal and R. Srikant, “On integrating catalogs,” In Proceedings of the tenth international conference on World Wide Web, ACM Press, pp. 603-612, 2001.
  10. [10] A. Doan, P. Domingos, and A. Halevy, “Learning to match the schemans of data sources: A ultistrategy approach,” VLDB Journal, Vol.50, pp. 279-301, 2003.
  11. [11] N. Guarino and P. Giaretta, “Ontologies and Knowledge bases: towards a terminological clarification,” In N. Mars (Ed.), “Toward Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing,” pp. 25-32, 1995.
  12. [12] X. Su, “A text categorization perspective for ontology mapping,” Technical report, Department of Computer and Information Science, Norwegian University of Science and Technology, Norway, 2002.

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