Abstract
The importance of research on knowledge management is growing due to recent issues with big data. The most fundamental steps in knowledge management are the extraction and construction of terminologies. Terms are often expressed in various forms and the term variations play a negative role, becoming an obstacle which causes knowledge systems to extract unnecessary knowledge. To solve the problem, we propose a method of term normalization which finds a normalized form (original and standard form defined in dictionaries) of variant terms. The method employs a couple of characteristics of terms: one is appearance similarity, which measures how similar terms are, and the other is context similarity which measures how many clue words they share. Through experiment, we show its positive influence of both similarities in the term normalization.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hwang, M., Jeong, DH., Jung, H., Sung, WK., Shin, J., Kim, P. (2012). A Term Normalization Method for Better Performance of Terminology Construction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_79
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DOI: https://doi.org/10.1007/978-3-642-29347-4_79
Publisher Name: Springer, Berlin, Heidelberg
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