Conference proceeding
Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning
Proceedings of the 14th ACM international conference on information and knowledge management, pp 249-250
31 Oct 2005
Abstract
Two complementary and non-interactive literature sets of articles, when they are considered together, can reveal useful information of scientific interest not apparent in either of the two document sets. Swanson called the existence of such knowledge, undiscovered public knowledge (UDPK). This paper proposes a semantic-based mining model for UDPK. Our method replaces manual ad-hoc pruning with using semantic knowledge from the biomedical ontologies. Using the semantic types and semantic relationships of the biomedical concepts, our prototype system can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The system successfully replicates Swanson's two famous discoveries: Raynaud disease/fish oils and migraine/magnesium. Compared with previous approaches, our methods generate much fewer but more relevant novel hypotheses, and require much less human intervention in the discovery procedure.
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5 citations in Scopus
Details
- Title
- Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning
- Creators
- Xiaohua HuIllhoi YooMin SongYanqing ZhangIl-Yeol Song
- Publication Details
- Proceedings of the 14th ACM international conference on information and knowledge management, pp 249-250
- Conference
- 14th ACM international conference on information and knowledge management, 14th
- Series
- CIKM '05
- Publisher
- Association for Computing Machinery (ACM)
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-33745777620
- Other Identifier
- 991014878194804721