A review of semantic similarity measures in biomedical domain using SNOMED-CT

Abstract

The determination of semantic similarity between word pairs is an important task in text understanding that supports the processing, classification and structuring of textual resources. In the field of biomedical, semantic similarity measures have been the focus of much research by exploiting knowledge sources such as domain ontologies. SNOMED-CT as a main biomedical ontology provides a global and broad hierarchical terminology for clinical data storage, encoding, and the retrieval of health and diseases information. In this study, we classified the measures proposed in biomedical domain and used SNOMED-CT as an input ontology. We also examined the studies that evaluated these methods using biomedical benchmarks. Regarding this, three major databases, including Science Direct, Springer and IEEE were selected to extract studies which proposed similarity measures and used SNOMED-CT as a knowledge source. The purpose of this study is to provide the reader with the understanding about the application of semantic similarity measures in biomedical domain using SNOMED-CT, and to gain a clear insight about the performance of these methods. This study also supports researchers and practitioners in effectively adapting semantic similarity measures in SNOMED-CT and provides an insight into its state-of-the-art.