A text-mining system for knowledge discovery from biomedical documents
| Autor: | Koichi Takeda, Naohiko Uramoto, Hironori Takeuchi, Akiko Murakami, Tohru Nagano, Hirofumi Matsuzawa |
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| Rok vydání: | 2004 |
| Předmět: |
Information retrieval
General Computer Science business.industry Computer science Concept mining Computer Graphics and Computer-Aided Design Biomedical text mining Theoretical Computer Science Set (abstract data type) Text mining Computational Theory and Mathematics Knowledge extraction Key (cryptography) IBM business Software Natural language Information Systems |
| Zdroj: | IBM Systems Journal. 43:516-533 |
| ISSN: | 0018-8670 |
| DOI: | 10.1147/sj.433.0516 |
| Popis: | This paper describes the application of IBM TAKMI® for Biomedical Documents to facilitate knowledge discovery from the very large text databases characteristic of life science and healthcare applications. This set of tools, designated MedTAKMI, is an extension of the TAKMI (Text Analysis and Knowledge MIning) system originally developed for text mining in customer-relationship-management applications. MedTAKMI dynamically and interactively mines a collection of documents to obtain characteristic features within them. By using multifaceted mining of these documents together with biomedically motivated categories for term extraction and a series of drill-down queries, users can obtain knowledge about a specific topic after seeing only a few key documents. In addition, the use of natural language techniques makes it possible to extract deeper relationships among biomedical concepts. The MedTAKMI system is capable of mining the entire MEDLINE® database of 11 million biomedical journal abstracts. It is currently running at a customer site. |
| Databáze: | OpenAIRE |
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