Knowledge Enhanced Representations to Reduce the Semantic Gap in Clinical Decision Support

The semantic gap between queries and documents is a longstanding problem in Information Retrieval (IR), and it poses a critical challenge for medical IR due to the large presence in the medical language of synonymous and polysemous words, along with context-specific expressions. Two main lines of work have emerged in the past years to tackle this issue: (i) the use of external knowledge resources to enhance query and document bag-of-words representations; and (ii) the use of semantic models, based on the distributional hypothesis, which perform matching on latent representations of documents and queries. The presented research investigates the use of external knowledge resources in both lines – with a focus on knowledge-enhanced unsupervised neural latent representations and their analysis in terms of effectiveness and semantic representativeness.