  Objective: This work aimed to demonstrate the effectiveness of a hybrid
approach based on Sentence BERT model and retrofitting algorithm to compute
relatedness between any two biomedical concepts. Materials and Methods: We
generated concept vectors by encoding concept preferred terms using ELMo, BERT,
and Sentence BERT models. We used BioELMo and Clinical ELMo. We used Ontology
Knowledge Free (OKF) models like PubMedBERT, BioBERT, BioClinicalBERT, and
Ontology Knowledge Injected (OKI) models like SapBERT, CoderBERT, KbBERT, and
UmlsBERT. We trained all the BERT models using Siamese network on SNLI and STSb
datasets to allow the models to learn more semantic information at the phrase
or sentence level so that they can represent multi-word concepts better.
Finally, to inject ontology relationship knowledge into concept vectors, we
used retrofitting algorithm and concepts from various UMLS relationships. We
evaluated our hybrid approach on four publicly available datasets which also
includes the recently released EHR-RelB dataset. EHR-RelB is the largest
publicly available relatedness dataset in which 89% of terms are multi-word
which makes it more challenging. Results: Sentence BERT models mostly
outperformed corresponding BERT models. The concept vectors generated using the
Sentence BERT model based on SapBERT and retrofitted using UMLS-related
concepts achieved the best results on all four datasets. Conclusions: Sentence
BERT models are more effective compared to BERT models in computing relatedness
scores in most of the cases. Injecting ontology knowledge into concept vectors
further enhances their quality and contributes to better relatedness scores.
