Journal article
Text mining: Generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology, Vol.55(5), pp.396-413
03/2004
DOI: 10.1002/asi.10389
Abstract
Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.
Details
- Title: Subtitle
- Text mining: Generating hypotheses from MEDLINE
- Creators
- Padmini Srinivasan
- Resource Type
- Journal article
- Publication Details
- Journal of the American Society for Information Science and Technology, Vol.55(5), pp.396-413
- Publisher
- Wiley Subscription Services, Inc., A Wiley Company; Hoboken
- DOI
- 10.1002/asi.10389
- ISSN
- 1532-2882
- eISSN
- 1532-2890
- Number of pages
- 18
- Language
- English
- Date published
- 03/2004
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003014502771
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