TY - GEN
T1 - Using a similarity measurement to partition a vocabulary of medical concepts
AU - Gu, Huanying Helen
AU - Geiler, James
AU - Liu, Li Min
AU - Halper, Michael
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - Controlled medical vocabularies have become increasingly important in a range of medical informatics applications. However, the extensive size of most vocabularies often makes it difficult for users to gain an understanding of their contents. In previous work, we have investigated the partitioning of a large semantic-network based medical vocabulary into smaller units, for the purpose of easier graphical display and comprehension. The partitioning process relied heavily on a domain expert. In this paper, we propose a structural method for automating the partitioning of a vocabulary. The structural method is based on a definition of the similarity of a pair consisting of a child concept and its parent concept in the semantic network. A distribution over these similarities for all pairs in the semantic network is then computed. Based on this distribution, the semantic network can be partitioned into more manageable pieces. The approach has been applied to the InterMED and a complex portion of the MED, two large medical vocabularies.
AB - Controlled medical vocabularies have become increasingly important in a range of medical informatics applications. However, the extensive size of most vocabularies often makes it difficult for users to gain an understanding of their contents. In previous work, we have investigated the partitioning of a large semantic-network based medical vocabulary into smaller units, for the purpose of easier graphical display and comprehension. The partitioning process relied heavily on a domain expert. In this paper, we propose a structural method for automating the partitioning of a vocabulary. The structural method is based on a definition of the similarity of a pair consisting of a child concept and its parent concept in the semantic network. A distribution over these similarities for all pairs in the semantic network is then computed. Based on this distribution, the semantic network can be partitioned into more manageable pieces. The approach has been applied to the InterMED and a complex portion of the MED, two large medical vocabularies.
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U2 - 10.1007/3-540-48309-8_66
DO - 10.1007/3-540-48309-8_66
M3 - Conference contribution
AN - SCOPUS:22844456549
SN - 3540664483
SN - 9783540664482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 712
EP - 723
BT - Database and Expert Systems Applications - 10th International Conference, DEXA 1999, Proceedings
A2 - Bench-Capon, Trevor J. M.
A2 - Soda, Giovanni
A2 - Tjoa, A. Min
PB - Springer Verlag
T2 - 10th International Conference on Database and Expert Systems Applications, DEXA 1999
Y2 - 30 August 1999 through 3 September 1999
ER -