  Finding concepts in large clinical ontologies can be challenging when queries
use different vocabularies. A search algorithm that overcomes this problem is
useful in applications such as concept normalisation and ontology matching,
where concepts can be referred to in different ways, using different synonyms.
In this paper, we present a deep learning based approach to build a semantic
search system for large clinical ontologies. We propose a Triplet-BERT model
and a method that generates training data directly from the ontologies. The
model is evaluated using five real benchmark data sets and the results show
that our approach achieves high results on both free text to concept and
concept to concept searching tasks, and outperforms all baseline methods.
