  Estimation of semantic similarity and relatedness between biomedical concepts
has utility for many informatics applications. Automated methods fall into two
categories: methods based on distributional statistics drawn from text corpora,
and methods using the structure of existing knowledge resources. Methods in the
former category disregard taxonomic structure, while those in the latter fail
to consider semantically relevant empirical information. In this paper, we
present a method that retrofits distributional context vector representations
of biomedical concepts using structural information from the UMLS
Metathesaurus, such that the similarity between vector representations of
linked concepts is augmented. We evaluated it on the UMNSRS benchmark. Our
results demonstrate that retrofitting of concept vector representations leads
to better correlation with human raters for both similarity and relatedness,
surpassing the best results reported to date. They also demonstrate a clear
improvement in performance on this reference standard for retrofitted vector
representations, as compared to those without retrofitting.
