  The search of information in large text repositories has been plagued by the
so-called document-query vocabulary gap, i.e. the semantic discordance between
the contents in the stored document entities on the one hand and the human
query on the other hand. Over the past two decades, a significant body of works
has advanced technical retrieval prowess while several studies have shed light
on issues pertaining to human search behavior. We believe that these efforts
should be conjoined, in the sense that automated retrieval systems have to
fully emulate human search behavior and thus consider the procedure according
to which users incrementally enhance their initial query. To this end,
cognitive reformulation patterns that mimic user search behaviour are
highlighted and enhancement terms which are statistically collocated with or
lexical-semantically related to the original terms adopted in the retrieval
process. We formalize the application of these patterns by considering a query
conceptual representation and introducing a set of operations allowing to
operate modifications on the initial query. A genetic algorithm-based weighting
process allows placing emphasis on terms according to their conceptual
role-type. An experimental evaluation on real-world datasets against relevance,
language, conceptual and knowledge-based models is conducted. We also show,
when compared to language and relevance models, a better performance in terms
of mean average precision than a word embedding-based model instantiation.
