Procedural knowledge extraction on MEDLINE abstracts

Authors:
Sa-kwang Song;Heung-seon Oh;Sung Hyon Myaeng;Sung-Pil Choi;Hong-Woo Chun;Yun-Soo Choi;Chang-Hoo Jeong
Affiliations:
Korea Institute of Science and Technology Information, Korea;Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea;Korea Institute of Science and Technology Information, Korea;Korea Institute of Science and Technology Information, Korea;Korea Institute of Science and Technology Information, Korea;Korea Institute of Science and Technology Information, Korea
Venue:
AMT'11 Proceedings of the 7th international conference on Active media technology
Year:
2011

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Abstract

Text mining is a popular methodology for building Technology Intelligence which helps companies or organizations to make better decisions by providing knowledge about the state-of-the-art technologies obtained from the Internet or inside companies. As a matter of fact, the objects or events (socalled declarative knowledge) are the target knowledge that text miners want to catch in general. However, we propose how to extract procedural knowledge rather than declarative knowledge utilizing machine learning method with deep language processing features, as well as how to model it. We show the representation of procedural knowledge in MEDLINE abstracts and provide experiments that are quite promising in that it shows 82% and 63% performances of purpose/solutions (two components of procedural knowledge model) extraction and unit process (basic unit of purpose/solutions) identification respectively, even though we applied strict guidelines in evaluating the performance.