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A term normalization method for efficient knowledge acquisition through text processing

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Abstract

The importance of research on knowledge management is growing due to recent issues on Big Data. One of the most fundamental steps in knowledge management is the extraction of terminologies. Terms are often expressed in various forms and the variations often play a negative role, becoming an obstacle which causes knowledge systems to extract unnecessary ones. 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 two characteristics of terms: appearance similarity measuring how similar terms are, context similarity measuring how many clue words they share. Through experiment, we show its positive influence of both similarities in term normalization.

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Notes

  1. Big Data - Wikipedia, The Free Encyclopedia: http://en.wikipedia.org/wiki/Big_data

  2. Wikipedia: The Free Encyclopedia: http://en.wikipedia.org/wiki/Main_Page

  3. This paper is an extension of the paper [9] presented in a workshop (IWEKSS 2012) held as part of an international conference (ICAISC 2012). And the paper contains qualitative upgrade over all with additional contents such as more examples for easy understanding and new experimental results with abundant test sets.

  4. NDSL (National Discovery for Science Leaders): http://www.ndsl.kr/index.do

  5. DBPedia: http://dbpedia.org/About

  6. The Stanford Natural Language Processing Group: http://nlp.stanford.edu/software/tagger.shtml

  7. The Porter Stemming Algorithm: http://tartarus.org/ martin/PorterStemmer/

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Authors and Affiliations

  1. 245 Daehak-ro, Yuseong-gu, Daejeon, 305-806, Republic of Korea

    Myunggwon Hwang, Do-Heon Jeong, Jinhyung Kim, Sa-Kwang Song & Hanmin Jung

  2. 375 Seoseok-dong, Dong-gu, Gwangju, 501-759, Republic of Korea

    Juhyun Shin & Pankoo Kim

Authors
  1. Myunggwon Hwang
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  2. Do-Heon Jeong
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  3. Jinhyung Kim
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  4. Sa-Kwang Song
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  5. Hanmin Jung
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  6. Juhyun Shin
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  7. Pankoo Kim
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Correspondence to Do-Heon Jeong.

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Hwang, M., Jeong, DH., Kim, J. et al. A term normalization method for efficient knowledge acquisition through text processing. Multimed Tools Appl 65, 75–91 (2013). https://doi.org/10.1007/s11042-012-1144-7

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  • DOI: https://doi.org/10.1007/s11042-012-1144-7

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