Abstract
The semantic web technology is seen as a key to realizing peer-to-peer for resource discovery and service combination in the ubiquitous communication environment. However, in a Peer-to-Peer environment, we must face the situation, where individual peers maintain their own view of the domain in terms of the organization of the local information sources. Ontology heterogeneity among individual peers is becoming ever more important issues. In this paper, we propose a multi-strategy learning approach to resolve the problem. We describe the SIMON (Semantic Interoperation by Matching between ONtologies) system, which applies multiple classification methods to learn the matching between ontologies. We use the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. On the prediction results of individual methods, the system combines their outcomes using our matching committee rule called the Best Outstanding Champion. The experiments show that SIMON system achieves high accuracy on real-world domain.
Research described in this paper is supported by The Science & Technology Committee of Shanghai Municipality Key Project Grant 02DJ14045 and by The Science & Technology Committee of Shanghai Municipality Key Technologies R&D Project Grant 03dz15027.
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References
Broekstra, J., Ehrig, M., Haase, P.: A Metadata Model for Semantics-Based Peer-to-Peer Systems. In: Proceedings of SemPGRID 2003, 1st Workshop on Semantics in Peer-to-Peer and Grid Computing (2003)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to Map between Ontologies on the Semantic Web. In: Proceedings of the World Wide Web Conference (WWW 2002) (2002)
Witten, I.H., Bell, T.C.: The zero-frequency problem: Estimating the probabilities of novel events in text compression. IEEE Transactions on Information Theory 37(4) (July 1991)
Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A midterm report. In: Proceedings of the European Conference on Machine Learning, Vienna, Austria, pp. 3–20 (1993)
Maedche, A., Staab, S.: Comparing Ontologies- Similarity Measures and a Comparison Study. Internal Report No. 408, Institute AIFB, University of Karlsruhe (March 2001)
Craven, M., DiPasquo, D., Freitag, D., McCalluma, A., Mitchell, T.: Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence. Elsevier, Amsterdam (1999)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1) (March 2002)
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© 2004 Springer-Verlag Berlin Heidelberg
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Pan, L., Zhang, L., Ma, F. (2004). SIMON: A Multi-strategy Classification Approach Resolving Ontology Heterogeneity – The P2P Meets the Semantic Web. In: Li, M., Sun, XH., Deng, Q., Ni, J. (eds) Grid and Cooperative Computing. GCC 2003. Lecture Notes in Computer Science, vol 3033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24680-0_120
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DOI: https://doi.org/10.1007/978-3-540-24680-0_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21993-4
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