Conference proceeding
Ontology based clustering for improving genomic IR
TWENTIETH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, pp 225-230
01 Jan 2007
Abstract
Recent work has shown that ontologv is useful to improve the performance of information retrieval, especially in biomedical literatures. The method of ontology-based can solve synonym problems. In this paper, we propose a new frame for genomic information retrieval based on UMLS. In our frame, Genomic information retrieval includes three processes: first, documents were indexed based UMLS, which means documents were represented by concepts, besides, the concept weight was re-calculated combined with similarity between concepts. Second, documents were clustered using fuzzy c-means method. At last cluster language model is utilized for information retrieval. Our method can solve partly synonymy and polysemy problems. The new method is evaluated on TREC 2004105 Genomics Track collections. Experiments show that the retrieval performance is greatly improved by the new method compared with the basic language model.
Metrics
Details
- Title
- Ontology based clustering for improving genomic IR
- Creators
- Jian Wen - University of DefenceZhoujun Li - Beihang UniversityXiaohua Hu - Drexel University
- Contributors
- P Kokol (Editor)Podgorelec (Editor)D MiceticTurk (Editor)M Zorman (Editor)M Verlic (Editor)
- Publication Details
- TWENTIETH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, pp 225-230
- Series
- IEEE International Symposium on Computer-Based Medical Systems
- Publisher
- IEEE
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000248094800038
- Scopus ID
- 2-s2.0-34748905876
- Other Identifier
- 991019167524304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Cybernetics
- Computer Science, Information Systems
- Engineering, Biomedical