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Document Retrieval using Concept Network  

Hur, Won-Chang (인하대학교 경영학부)
Lee, Sang-Jin (삼성네트웍스 기술솔루션사업팀)
Publication Information
Asia pacific journal of information systems / v.16, no.4, 2006 , pp. 203-215 More about this Journal
Abstract
The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.
Keywords
Document Management; Information Retrieval; Concept Network; Knowledge Management;
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  • Reference
1 Alavi, M. and Leidner, D., "Knowledge Management Systems: Emerging Views and Practices from the Field," In IEEE Proceedings of the 32nd Hawaii International Conference on System Sciences, pp.1-11, 1999
2 Fraeks. W. and Baeza-Yates, R., Information Retrieval: Data Structures and Algorithms, Prentice-Hall, 1992
3 Gopal, B. and Manber, U., Integrating Content-Based Access Mechanisms with Hierarchical File Systems, In ACM OSDI, pp. 265-278, 1999
4 Kim, Y. H. et al., "InfoFlow: A Web- based Workflow Management System," Proceedings of International Conference CALS/ EC Korea'99, 1999
5 Singhal, A., Salton, G., Mitra, M., and Buckley, C. "Document length normalization," Information Processing and Management, Vol. 32, No. 5, pp 619-633, 1996   DOI   ScienceOn
6 Tiwana, A., "Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Plafrom," Prentice Hall, Upper Saddle River, NJ 07458, 2002
7 Gudivada, V.N. and Raghavan, V.V., Design and evaluation of algorithms for image retrieval by spatial similarity, ACM Transaction on Information Systems, Vol. 13, No. 2, April, pp. 115-144, 1995   DOI   ScienceOn
8 Lewis, D.D., "Representation and Learning in Information Retrieval," University of Massachusetts, 1992
9 Grossman, D.A. and Frieder, O., "Information Retrieval: Algorithms and Heuristics," Kluwer Academic Publishers, 1998
10 Combarro, E.F., Montanes, E., Diaz, I., Ranilla, J, and Mones, R., "Introducing a Family of Linear Measures for Feature Selection in Text Categorization," IEEE Transaction on Knowledge and Data Engineering, Vol. 17, No. 9, 2005
11 Zimmermann, H. J., Fuzzy Set Theory - and Its Applications, Kluwer Academic Publishers, 1991
12 Kalt, T., "A New Probabilistic Model of Text Classification and Retrieval", University of Massachusetts, 1996
13 Chen, S.M., Wang, J.Y., "Fuzzy Query Processing for Document Retrieval Based on Extended Fuzzy Concept Network," Transactions on Systems, Man, and Cybernetics, Vol. 29, No. 1, February 1999
14 Gudivada, V. N., Reaghavan, V. V., Grosky, W. I., and Kasanagottu, R., "Information Retrieval On The World Wide Web," IEEE Internet Computing, September. October 1997
15 Awad, E.M. and Ghaziri, H. M., Knowledge Management, Prentice Hall, Upper Saddle River, NJ 07458, 2004
16 Chen, H., "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms," MIS Department, College of Business and Public Administration, University of Arizona, July 1994
17 Sebastiani, F., "Machine Learning in Automated Text Categorization," ACM Computing Surveys, Vol. 34, No. 1, pp. 1-47, Mar. 2002   DOI   ScienceOn
18 Faloutsos, C., Orad, D., "A Survey of Information Retrieval and Filtering Methods," University of Maryland, College Park, MD20742, 1994
19 Brin, S. and Page, L., "The anatomy of a large-scale hypertextual Web search engine," Proceedings of the seventh international conference on World Wide Web 7, pp. 107-117, 1998   DOI   ScienceOn
20 Miller, G. A., Beckwith, R., "Introduction to Word-Net: An On-line Lexical Database," Princeton University,1993
21 Luhn, H., Keyword in Context Index for Technical Literature, American Documentation, XI (4), 1960
22 Bartell, B.T. et al., "Optimizing Parameters in a Ranked Retrieval System Using Multi-Query Relevance Feedback," In the Proceeding of 3rd Annual Symposium on Document Analysis and Information Retrieval, 1992