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Published Articles >> Table of Contents >> Abstract
December 1993 (Vol. 8, No. 6)
pp. 27-38
Extracting Knowledge from Diagnostic Databases
Ramasamy Uthurusamy
Linda G. Means
Kurt S. Godden
Steven L. Lytinen
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.248350
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| Abstract |
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The use of natural language processing and machine learning techniques to help interpret, characterize, and standardize data, thereby enhancing the extraction of knowledge from diagnostic databases, is discussed. In particular Lexfix, a vocabulary correction and standardization system, has been designed to improve keyword-based retrieval on free-form text fields in GM's Technical Assistance System (TAS) database, which contains about 300000 cases of vehicle symptoms and repair information. Also implemented was a natural language parser called TASLink, designed to interpret various kinds of ill-formed English, particularly free-form descriptions of vehicle faults. Inferule, an inductive machine-learning system that infers diagnostic rules from database cases containing information about vehicle symptoms and their solutions, is also described.
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 References
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[1] L.G. Means, "Cn Yur Cmputr Raed Ths?,"Proc. Second Conf. Applied Natural Language Processing, ACL, Morristown, N.J., 1988, pp. 93-100.
[2] S. Lytinen, "Robust Processing of Terse Text,"Proc. 1990 AAAI Symp. Intelligent Text-based Systems, AAAI Press, Menlo Park, Calif., 1990, pp. 10-14.
[3] C. Pollard and I. Sag,Information-Based Syntax and Semantics, Vol. 1, Center for the Study of Language and Information, Menlo Park, Calif., 1987, Chapter 4.
[4] C. Mellish, "Some Chart-Based Techniques for Parsing Ill-Formed Input,"Proc. 1989 Conf. Assoc. for Computational Linguistics, ACL, Morristown, N.J., 1989, pp. 102-109.
[5] W. Woods, "Optimal Search Strategies for Speech Understanding Control,"Artificial Intelligence, Vol. 18, No. 3, 1982, pp. 295- 326.
[6] K. Godden, "Lazy Unification,"Proc. 28th Ann. Meeting of the Assoc. for Computational Linguistics, ACL, Morristown, N.J., 1990, pp. 180-187.
[7] K. Godden, "Feature-Based Design Evaluation Through Constraint Structure Unification,"Proc. Third Int'l Conf. Tools for Artificial Intelligence, IEEE CS Press, Los Alamitos, Calif., 1991, pp. 119-124.
[8] J. R. Quinlan, "Induction of decision trees,"Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[9] L. Breiman et al.,Classification and Regression Trees, Wadsworth and Brooks-Cole, Monterey, Calif., 1984.
[10] J. Quinlan, "Simplifying decision trees,"Int. J. Man-Machine Studies, vol. 27, pp. 221-234, 1987.
[11] S. Spangler, U.M. Fayyad, and R. Uthurusamy, "Induction of Decision Trees from Inconclusive Data,"Proc. Sixth Int'l Workshop on Machine Learning, Morgan Kaufmann, San Mateo, Calif., 1989, pp. 146-150.
[12] R. Uthurusamy, U.M. Fayyad, and S. Spangler, "Learning Useful Rules from Inconclusive Data,"Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley, eds., AAAI Press, Menlo Park, Calif., 1991, pp. 141-157.
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 Additional Information
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Citation:
Ramasamy Uthurusamy, Linda G. Means, Kurt S. Godden, Steven L. Lytinen,
"Extracting Knowledge from Diagnostic Databases,"
IEEE Expert: Intelligent Systems and Their Applications,
vol. 08,
no. 6,
pp. 27-38,
Dec.,
1993
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