Comparison of Ontology-Based Semantic-Similarity Measures in the Biomedical Text

Abstract

In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and evaluations. The major contribution of this paper is to choose the best measure among different similarity measures that give us good result with less error rate and that can be used in our Benchmark model. The benchmark means an evaluation of the quality of onto logies in the health domains according to their semantic similarities which are similar or not similar. The main goal of the benchmark is to evaluate ontologies in the health domain using ICD10 as aknowledge source. The experiment was done on a taxonomy built to measure the semantic distance between two concepts in the health domain, which are represented as nodes in the taxonomy. Similarity measures methods were evaluated relative to human experts’ ratings. Our experiment was applied on the ICD10 taxonomy to determine the similarity value between two concepts. The similarity between 30 pairs of the health domains has been evaluated using different types of semantic similarity measures equations. The experimental results discussed in this paper have shown that the Hoa A. Nguyen and Hisham Al-Mubaid measure has achieved high matching score by the expert’s judgment.