An Intelligent Method for Expert Finding Based on Knowledge Organization Systems: Taking the Example of Oncology. Issue 2 (June 2019)
- Record Type:
- Journal Article
- Title:
- An Intelligent Method for Expert Finding Based on Knowledge Organization Systems: Taking the Example of Oncology. Issue 2 (June 2019)
- Main Title:
- An Intelligent Method for Expert Finding Based on Knowledge Organization Systems: Taking the Example of Oncology
- Authors:
- Pei-yan, Song
Dong-fang, Wang
Yan, Cao - Abstract:
- Abstract: In this paper, the experts are mapped and found through the concept and semantic relations of knowledge organization systems. First, a hierarchical model of expert description is designed based on the literature data and knowledge organization systems, as foundation of expert rapid finding, intelligent reasoning and dynamic updating. Then, 5 key steps are introduced based on knowledge organization systems, by which the research directions of experts can be identified accurately and visually in TF-IDF and LDA model. Finally, taking the example of Oncology, the experiment indicates that experts with the same or near research direction can be aggregated fast and effectively by semantic linkage compared with VSM agritourism. This method is expected to find out the most suitable experts by calculation and reasoning automatically.
- Is Part Of:
- Journal of physics. Volume 1213:Issue 2(2019)
- Journal:
- Journal of physics
- Issue:
- Volume 1213:Issue 2(2019)
- Issue Display:
- Volume 1213, Issue 2 (2019)
- Year:
- 2019
- Volume:
- 1213
- Issue:
- 2
- Issue Sort Value:
- 2019-1213-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-06
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1213/2/022020 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11111.xml