  Semantic concepts and relations encoded in domain-specific ontologies and
other medical semantic resources play a crucial role in deciphering terms in
medical queries and documents. The exploitation of these resources for tackling
the semantic gap issue has been widely studied in the literature. However,
there are challenges that hinder their widespread use in real-world
applications. Among these challenges is the insufficient knowledge individually
encoded in existing medical ontologies, which is magnified when users express
their information needs using long-winded natural language queries. In this
context, many of the users query terms are either unrecognized by the used
ontologies, or cause retrieving false positives that degrade the quality of
current medical information search approaches. In this article, we explore the
combination of multiple extrinsic semantic resources in the development of a
full-fledged medical information search framework to: i) highlight and expand
head medical concepts in verbose medical queries (i.e. concepts among query
terms that significantly contribute to the informativeness and intent of a
given query), ii) build semantically enhanced inverted index documents, iii)
contribute to a heuristical weighting technique in the query document matching
process. To demonstrate the effectiveness of the proposed approach, we
conducted several experiments over the CLEF eHealth 2014 dataset. Findings
indicate that the proposed method combining several extrinsic semantic
resources proved to be more effective than related approaches in terms of
precision measure.
