Abstract
Ontologies are of vital importance to the successful realization of semantic Web. Currently, the existing concepts in ontologies are not approximate but clear. However, in real application domains many concepts are difficult to define explicitly. In order to fulfill semantic Web, it’s not only necessary but also important to study approximate concepts and approximate ontologies generated from the approximate concepts. In this paper, based on the principle of granular computing, a granulation model for representing approximate ontologies was constructed. Then algorithms for capturing approximate concepts and generating approximate ontologies were proposed and illustrated with a real example.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Klinov, P., Mazlack, L.J.: Granulating Semantic Web Ontologies. In: The Proceedings of International Conference on Granular Computing, IEEE, Atlanta, Georgia, USA, pp. 225–229 (2006)
Stoilos, G., et al.: Fuzzy OWL: Uncertainty And The Semantics Web. In: Proc. Of the International Workshop on OWL: Experience and Directions (2005)
Straccia, U.: Towards A Fuzzy Description Logic For The semantic Web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 167–181. Springer, Heidelberg (2005)
Doherty, P., et al.: Towards A Framework For Approximate Ontologies. Fundamenta Informaticae 57, 147–165 (2003)
Liu, Q.: Rough Sets and Rough Reasoning. Science Press, Beijing (2003)
Liu, Q.: Granules and Applications of Granular Computing in Logical Reasoning (in Chinese). Journal of Computer Research and Development 41, 546–551 (2004)
Zadeh, L.A.: Fuzzy Sets and Information Granularity. In: Gupta, M., Ragade, R., Yager, R. (eds.) Advances in Fuzzy Sets Theory and Applications, pp. 3–18. North Holland, Amsterdam (1979)
Stepaniuk, J., Skowron, A.: Ontological Framework for Approximation. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 718–727. Springer, Heidelberg (2005)
Yao, Y.Y.: Perspectives of Granular Computing. In: Proceedings of IEEE International Conference on granular computing, Beijing, China, pp. 85–90 (2005)
Studer, R., Benjamins, V.R., Fensel, D.: Knowledge Engineering Principles and Methods. Data and Knowledge Engineering 25, 161–197 (1998)
Poli, P.: Ontological Methodology. Int. J. Human-Computer Studies 56, 639–664 (2002)
Deng, Z.H., Tang, S.W., Zhang, M.: Overview of Ontology. Journal of Peking University (natural Science, In Chinese) 38, 730–737 (2002)
Yao, Y.Y.: Granular Computing (in Chinese). Computer Science 31, 1–5 (2004)
Lin, T.Y.: Granular Computing II: Infrastructures for AI-Engineering. In: The Proceedings of International Conference on Granular Computing, IEEE, Atlanta, USA, pp. 2–7 (2006)
Du, X.Y., Li, M., Wang, S.: A Survey on Ontology Learning Research (in Chinese). Journal of Software 17, 1837–1847 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Qiu, T., Chen, X., Liu, Q., Huang, H. (2007). Granulation Based Approximate Ontologies Capture. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_53
Download citation
DOI: https://doi.org/10.1007/978-3-540-72458-2_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
eBook Packages: Computer ScienceComputer Science (R0)