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A Cluster-Based Approach for Semantic Similarity in the Biomedical Domain

Summary: A Cluster-Based Approach for Semantic Similarity in the
Biomedical Domain
Hisham Al-Mubaid and Hoa A. Nguyen
University of Houston-Clear Lake, Houston, TX 77058, USA
{hisham, nguyenh3308}@uhcl.edu
Abstract--We propose a new cluster-based semantic
similarity/distance measure for the biomedical domain within
the framework of UMLS. The proposed measure is based
mainly on the cross-modified path length feature between the
concept nodes, and two new features: (1) the common
specificity of two concept nodes, and (2) the local granularity
of the clusters. We also applied, for comparison purpose, five
existing general English ontology-based similarity measures
into the biomedical domain within UMLS. The proposed
measure was evaluated relative to human experts' ratings,
and compared with the existing techniques using two
ontologies (MeSH and SNOMED-CT) in UMLS. The
experimental results confirmed the efficiency of the proposed
method, and showed that our similarity measure gives the best
overall results of correlation with human ratings. We show,


Source: Al-Mubaid, Hisham - School of Science and Computer Engineering, University of Houston-Clear Lake


Collections: Computer Technologies and Information Sciences