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Customized Knowledge Creation Framework using Context- and intensity-based Similarity  

Sohn, Mye M. (성균관대학교시스템경영공학과)
Lee, Hyun-Jung (서강대학교)
Publication Information
Journal of Internet Computing and Services / v.12, no.5, 2011 , pp. 113-125 More about this Journal
Abstract
As information resources have become more various and the number of the resources has increased, knowledge customization on the social web has been becoming more difficult. To reduce the burden, we offer a framework for context-based similarity calculation for knowledge customization using ontology on the CBR. Thereby, we newly developed context- and intensity-based similarity calculation methods which are applied to extraction of the most similar case considered semantic similarity and syntactic, and effective creation of the user-tailored knowledge using the selected case. The process is comprised of conversion of unstructured web information into cases, extraction of an appropriate case according to the user requirements, and customization of the knowledge using the selected case. In the experimental section, the effectiveness of the developed similarity methods are compared with other edge-counting similarity methods using two classes which are compared with each other. It shows that our framework leads higher similarity values for conceptually close classes compared with other methods.
Keywords
Knowledge Customization; Social Web; Context-based Similarity; Case-based reasoning; Ontology;
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  • Reference
1 Resnik P., Using information content to evaluate semantic similarity in a taxonomy, Proc. 14th International Joint Conference on Artificial Intelligence, vol. 1, 1995, pp. 448-453
2 A. P. Bernstein and L. Haas, "Information Integration in the Enterprise," Communications of the ACM, vol. 51, no. 9, 2008,pp. 72-79.   DOI   ScienceOn
3 V. Alexiev, et al., "Information Integration with Ontologies: Experiences from an Industrial Showcase,"Wiley, 2005
4 Y. A. Halevy, et al., "Enterprise information integration:challenges and controversies," Proceedings of the 2005 ACM SIGMOD international conference on Management of data, 2005, pp. 778-787.
5 M. Philippe and P. W. Eklund, "Knowledge Retrieval and the World Wide Web," IEEE Intelligent Systems, 2000 (May/June), pp.18-25.
6 I. Mani and E. Bloedorn, "Machine learning of Generic and User-Focused Summarization," In proceedings of the National Conference on Artificial Intelligence (AAAI), 1998, pp. 820-826.
7 S. Teufel and K. Moens, "Sentence extraction as a classification task," Workshop of Intelligent and scalable text summarization, ACL/EACL 1997, 1997, pp.58-65.
8 T. Slimani, B. B.Yaghlane, and K. Mellouli, "A New Similarity Measure based on Edge Counting," World Academy of Science, Engineering and Technology 23 2006, 2006, pp 34-38.
9 L. Song, et al., "Fuzzy Semantic Similarity Between Ontological Concepts," Advances and Innovations in Systems, Computing Sciences and Software Engineering, 2007, pp 275-280.
10 R. Rada,, Mili, H., Bicknell, E., Blettner, M., "Development and application of a metric on semantic nets," IEEE Transactions on Systems, Man, and Cybernetics vol. 19, no. 1, 1989, pp. 17-30   DOI   ScienceOn
11 A. Hliaoutakis, Varelas G., Voutsakis E., Petrakis E. G. M., Milios E., "Information Retrieval by Semantic Similarity", International Journal on Semantic Web & Information Systems, vol. 2, 2006, pp. 55-73