Abstract
This paper describes an e-commerce application build on the Electronic Trading Opportunities System. This system enables ‘Trade Points’ and trade related bodies to exchange information by e-mail. This environment offers an enormous trade potential and opportunities to small and medium enterprises, but its efficiency is limited since the amount of circulating messages surpasses the human limit to analyze them. The application described here aids this process of analysis, allowing the extraction of the most relevant characteristics from the messages. The application is structured in three phases. The first is responsible for analyzing and for providing structural information about texts. The second identifies relevant information on texts through clustering and categorization processes. The third applies Information Extraction techniques, which are aided by the use of a domain specific knowledge base, to transform the unstructured information into a structured one. By the end, the user gets more quality in the analysis and can more easily find interesting ideas, trends and details, creating new trade opportunities to small and medium enterprises.
This research is partially sponsored by grants from CNPq and CAPES.
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References
UNTPDC. Electronic Trading Opportunities (ETO) System, United Nations Trade Point Development Center, UNTPDC (Last Access Date: September 2002), http://www.wtpfed.org
Han, J., Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases. In: Proc. of 1995 Int’l Conf. on Very Large Data Bases (VLDB1995), Zürich, Switzerland, September 1995, pp. 420–431 (1995)
Hobbs, J.R.: Generic Information Extraction System. Artificial Intelligence Center SRI International (2002), http://www.itl.nist.gov/iaui/894.02/related_projects/tipster/gen_ie.htm , (Last Access Date: September 2002)
Zaïane, O.R.: From Resource Discovery to Knowledge Discovery on the Internet, Technical Report TR 1998-13, Simon Fraser University (August 1998)
Hardy, D.R., Schwartz, M.F.: ESSENCE: A Resource Discovery System Based on Semantic File Indexing. In: USENIX WINTER CONVERENCE, San Diego, California, Boulder, University of Colorado, pp. 361–374 (1993)
Loh, S., Wives, L.K., Oliveira, J.P.M.: Concept-based knowledge discovery in texts extracted from the WEB. ACM SIGKDD Explorations 2(1), 29–39 (2000)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980); Reprinted in Karen, S.J., Willet, P.: Readings in Information Retrieval. Morgan Kaufmann, San Francisco (1997) ISBN 1-55860-454-4
Rocchio, J.J.: Document Retrieval Systems: Optimization and Evaluation, Ph.D. thesis, National Science Foundation, Harvard Computation Laboratory (1966)
Cohen, W.W., Singer, Y.: Context-Sensitive Learning Methods for Text Categorization. ACM TOIS 17(2), 141–173 (1999)
Ragas, H., Koster, C.: Four Text Classification Algorithms Compared on a Dutch Corpus. In: ACM-SIGIR 1998, pp. 369–370. ACM Press, New York (1998)
Apté, C., Damerau, F., Weiss, S.M.: Automated learning of decision rules for text categorization. ACM Transactions on Information Systems 12(3), 233–251 (1994)
Lehnert, W.: Crystal: Learning Domain-specific Text Analysis Rules. CIIR Technical Report Computer (1996), http://www-nlp.cs.umass.edu/ciir-pubs/te-43.pdf (Last Access Date: september 2002)
Grishman, R.: Information Extraction: Techniques and Challenges - Information Extraction - A Multidisciplinary Approach to an Emerging Information Technology. In: Pazienza., M.T. (ed.). LNCS (LNAI), pp. 10–27. Springer, Heidelberg (1997)
Constantino, M., Morgan, R.G., Collingham, R.J.: Financial Information Extraction Using Pre-defined and User-definable Templates in the LOLITA. CIT - Journal of Computing and Information Technology 4(4), 241–255 (1996)
Moulin, B., Rousseau, D.: Automated knowledge acquisition from regulatory texts. IEEE Expert 7(5), 27–35 (1992)
Cowie, J., Lehnert, W.: Information Extraction. Communications of the ACM 39(1), 80–91 (1996)
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Scarinci, R.G., Wives, L.K., Loh, S., Zabenedetti, C., de Oliveira, J.P.M. (2003). Managing Unstructured E-Commerce Information. In: Olivé, A., Yoshikawa, M., Yu, E.S.K. (eds) Advanced Conceptual Modeling Techniques. ER 2002. Lecture Notes in Computer Science, vol 2784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45275-1_36
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DOI: https://doi.org/10.1007/978-3-540-45275-1_36
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