Conference proceeding
A semantic-based approach for mining undiscovered public knowledge from biomedical literature
2005 IEEE International Conference on Granular Computing, v 1, pp 22-27
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The problem of mining undiscovered public knowledge from biomedical literature was exemplified by Swanson's pioneering work on Raynaud disease/fish-oil discovery in 1986. Since then, there have been many approaches to mine undiscovered public knowledge from biomedical literature. This paper presents a semantic-based approach for mining undiscovered public knowledge from biomedical literature. The method takes advantages of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system Biomedical Semantic-based Knowledge Discovery System (Bio-SbKDS) is designed to uncover novel hypothesis/connections hidden in the biomedical literature. Using the semantic types and semantic relations of the biomedical concepts, Bio-SbKDS can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. Bio-SbKDS successfully replicates Dr. Swanson's two famous discoveries: Raynaud disease/fish oil and migraine/magnesium. Compared with previous approaches, our method searches much less articles, generates much less but more relevant novel hypotheses, requires much less human intervention in the discovery procedure.
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Details
- Title
- A semantic-based approach for mining undiscovered public knowledge from biomedical literature
- Creators
- Xiaohua Hu - Drexel UniversityGuangrong Li - Drexel UniversityI Yoo - College of AccountingXiaodan Zhang - Drexel UniversityXuheng Xu - Drexel University
- Publication Details
- 2005 IEEE International Conference on Granular Computing, v 1, pp 22-27
- Conference
- 2005 IEEE International Conference on Granular Computing
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000232157200007
- Scopus ID
- 2-s2.0-33845339278
- Other Identifier
- 991019170611704721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods