  Poor information retrieval performance has often been attributed to the
query-document vocabulary mismatch problem which is defined as the difficulty
for human users to formulate precise natural language queries that are in line
with the vocabulary of the documents deemed relevant to a specific search goal.
To alleviate this problem, query expansion processes are applied in order to
spawn and integrate additional terms to an initial query. This requires
accurate identification of main query concepts to ensure the intended search
goal is duly emphasized and relevant expansion concepts are extracted and
included in the enriched query. Natural language queries have intrinsic
linguistic properties such as parts-of-speech labels and grammatical relations
which can be utilized in determining the intended search goal. Additionally,
extrinsic language-based resources such as ontologies are needed to suggest
expansion concepts semantically coherent with the query content. We present
here a query expansion framework which capitalizes on both linguistic
characteristics of user queries and ontology resources for query constituent
encoding, expansion concept extraction and concept weighting. A thorough
empirical evaluation on real-world datasets validates our approach against
unigram language model, relevance model and a sequential dependence based
technique.
