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Overcoming an obstacle in expanding a UMLS semantic type extent
Yan Chen1, Huanying Gu, Yehoshua Perl
1CIS Department, Borough of Manhattan Community College, CUNY, 199 Chamber Street, New York, NY 10007, USA. ychen@bmcc.cuny.edu
Journal of Biomedical Informatics
|September 20, 2011
Summary
A new method improves concept discovery in the Unified Medical Language System (UMLS), overcoming obstacles that previously hindered semantic type assignment for biomedical concepts.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
- Knowledge Representation
Background:
- Previous methods for identifying missing semantic type assignments in the UMLS faced challenges.
- An obstacle involving concepts classified under the UMLS Semantic Network's 'Classification' semantic type hindered accuracy.
Purpose of the Study:
- To develop a novel expansion methodology to overcome obstacles in concept discovery.
- To improve the accuracy and efficiency of assigning semantic types to UMLS concepts.
Main Methods:
- Designed a new methodology to bypass obstacles without causing combinatorial explosion.
- Tested the new methodology using the 'Experimental Model of Disease' semantic type.
Main Results:
- Identified over 500 concepts missed by the previous methodology for 'Experimental Model of Disease'.
- Demonstrated the general applicability of the methodology to other semantic types facing similar obstacles.
Conclusions:
- Algorithmic discovery of concepts needing semantic type assignment is feasible despite obstacles.
- The new methodology enhances the completeness of UMLS semantic type assignments efficiently.
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