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http://dx.doi.org/10.9708/jksci.2012.17.2.139

Probabilistic filtering for a biological knowledge discovery system with text mining and automatic inference  

Lee, Hee-Jin (Dept. of Computer Science, KAIST)
Park, Jong-C. (Dept. of Computer Science, KAIST)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.2, 2012 , pp. 139-147 More about this Journal
Abstract
In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.
Keywords
knowledge discovery system; text mining; automatic inference; probabilistic filtering;
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