IJCATR Volume 7 Issue 8

Evaluating Semantic Similarity between Biomedical Concepts/Classes through Single Ontology

Abdelhakeem M. B. Abdelrahman , Dr. Ahmad Kayed
10.7753/IJCATR0708.1008
keywords : Biomedical information retrieval, biomedical ontology, semantic similarity measures, Unified Medical Language System (UMLS).

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Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings.
@artical{a782018ijcatr07081009,
Title = "Evaluating Semantic Similarity between Biomedical Concepts/Classes through Single Ontology",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "7",
Issue ="8",
Pages ="292 - 369",
Year = "2018",
Authors ="Abdelhakeem M. B. Abdelrahman , Dr. Ahmad Kayed"}
  • Semantic similarity measures in single ontology
  • SemDist technique proposed by Hisham and H. Nguyen gives the best overall results of correlation with experts’ ratings
  • Using ICD-10 V1.0 ontology in our experiments.