TITLE:
A Survey on Semantic Similarity Measures between Concepts in Health Domain
AUTHORS:
Abdelhakeem M. B. Abdelrahman, Ahmad Kayed
KEYWORDS:
Biomedical Ontology, Semantic Similarity, Biomedical Concept, Unified Medical Language System (UMLS)
JOURNAL NAME:
American Journal of Computational Mathematics,
Vol.5 No.2,
June
26,
2015
ABSTRACT: The similarity between biomedical terms/concepts is a very important task for biomedical information extraction and knowledge discovery. The measures and tests are tools used to define how to measure the goodness of ontology or its resources. The semantic similarity measuring techniques can be classified into three classes: first, measuring semantic similarity using ontology/ taxonomy; second, using training corpora and information content and third, combination between them. Some of the semantic similarity measures are based on the path length between the concept nodes as well as the depth of the LCS node in the ontology tree or hierarchy, and these measures assign high similarity when the two concepts are in the lower level of the hierarchy. However, most of the semantic similarity measures can be adopted to be used in health domain (Biomedical Domain). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two concepts within single ontology or multiple ontologies in UMLS Metathesaurus (MeSH, SNOMED-CT, ICD), and compare my results to human experts score by correlation coefficient.