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Estimation of semantic similarity and relatedness between biomedical conceptshas utility for many informatics applications. Automated methods fall into twocategories: methods based on distributional statistics drawn from text corpora,and methods using the structure of existing knowledge resources. Methods in theformer category disregard taxonomic structure, while those in the latter failto consider semantically relevant empirical information. In this paper, wepresent a method that retrofits distributional context vector representationsof biomedical concepts using structural information from the UMLSMetathesaurus, such that the similarity between vector representations oflinked concepts is augmented. We evaluated it on the UMNSRS benchmark. Ourresults demonstrate that retrofitting of concept vector representations leadsto better correlation with human raters for both similarity and relatedness,surpassing the best results reported to date. They also demonstrate a clearimprovement in performance on this reference standard for retrofitted vectorrepresentations, as compared to those without retrofitting.

Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness