Research on Domain Ontology-Based Concept Similarity Calculation

Abstract:

Similarity computing of ontological concept has made rapid progress in the field of data mining, information processing and artificial intelligence and becoming one of the hot research field of information technology, particularly the idea of the semantic Web was proposed in 2000, the concept of semantic similarity has gotten more attention, while also facilitating its further development and application in information retrieval. Considering the deficiencies of existing concept similarity algorithm, this paper design the method to reduce the candidate set of domain concept, and put forward a similarity calculation model based on the concept name, instances, properties, and semantic structure of domain ontology. Integrated several main influencing factors, the experiments show the proposed algorithm can express the impact of various factors on the similarity in the calculation concept similarity of domain ontology. By comparing with the traditional similarity method and expertise experience value, the experiment result shows that the effectiveness and correctness of the concept similarity calculation model.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3512-3516
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3512
• Online since: January 2013
• Authors: Wen Jie Li, Sha Sha Shi, Si Liu
• Keywords: Concept Similarity, Domain Ontology, Semantic Structure

References

[1] Zh.H. Deng; Sh.W. Tang and M. Zhang: Overview of Ontology, Journal of Peking University (Natural Science), Vol. 38, No. 5 (2002), pp.730-738.

[2] A. Maedche and S. Staab, in: Measuring Similarity Between Ontologies, Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management, Springer- Verlag, London (2003), pp.251-263.

DOI: 10.1007/3-540-45810-7_24

[3] Zh.Y. Feng; W.J. Li and X.H. Li: Ontologies Engineering and Its Application (Tsinghua University Press, Beijing 2007).

[4] A.H. Doan; J. Madhavan and R. Dhamankar: Learning to Match Ontologies on the Semantic Web, The VLDB Journal, Vol. 12 (2003), pp.303-319.

DOI: 10.1007/s00778-003-0104-2

[5] R. Tous and J. Delgado: A Vector Space Model for Semantic Similarity Calculation and OWL Ontology Alignment, Workshop on Database and Expert Systems Applications, Lecture Notes in Computer Science, (2006), pp.307-316.

DOI: 10.1007/11827405_30

[6] W.J. Li; Y. Zhao and N. Shen, in: Concept Similarity Calculation in Ontology Mapping, Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, Vol. 2 (2009), pp.214-218.

DOI: 10.1109/fskd.2009.351

[7] R. Studer; V.R. Benjamins and D. Fensel: Knowledge Engineering, Principles and Methods, Journal of Data and Knowledge Engineering, Vol. 25, No. 1-2 (1998), pp.161-197.

DOI: 10.1016/s0169-023x(97)00056-6

[8] J. Chen and Z.H. Jiang: The Concept Similarity Calculation of Domain Ontology, Computer Engineering and Applications, Vol. 33 (2006), pp.163-166.

[9] W.J. Li and Q.X. Xia: A Method of Concept Similarity Computation Based on Semantic Distance, Procedia Engineering, Elsevier Ltd, Vol. 15 (2011), pp.3854-3859.

DOI: 10.1016/j.proeng.2011.08.721

[10] W.J. Li; Q.X. Xia and Y. Zhao, in: Study of Concept Similarity Algorithm Based on Ontology Structure, Proceedings of the 1st International Conference on E-Business and E-Government, Guangzhou, China (2010), pp.1448-1451.

DOI: 10.1109/icee.2010.368

[11] D. Lin, in: An Information-Theoretic Definition of Similarity, Proceedings of the International Conference on Machine Learning, (1998), pp.296-304.

[12] Tervsky: Features of Similarity, Psychological Review, Vol. 84, No. 4 (1977), pp.327-352.

[13] M. Ehrig and Y. Sure, in: Ontology Mapping-an Integrated Approach, Proceedings of the 1st European Semantic Web Symposium, Springer-Verlag, Berlin (2004), pp.76-91.

[14] W3C, in: Wine Ontology, http: /www. w3. org/TR/2003/PR-owl-guide-20031209/wine.