  Traditional information retrieval systems rely on keywords to index documents
and queries. In such systems, documents are retrieved based on the number of
shared keywords with the query. This lexical-focused retrieval leads to
inaccurate and incomplete results when different keywords are used to describe
the documents and queries. Semantic-focused retrieval approaches attempt to
overcome this problem by relying on concepts rather than on keywords to
indexing and retrieval. The goal is to retrieve documents that are semantically
relevant to a given user query. This paper addresses this issue by proposing a
solution at the indexing level. More precisely, we propose a novel approach for
semantic indexing based on concepts identified from a linguistic resource. In
particular, our approach relies on the joint use of WordNet and WordNetDomains
lexical databases for concept identification. Furthermore, we propose a
semantic-based concept weighting scheme that relies on a novel definition of
concept centrality. The resulting system is evaluated on the TIME test
collection. Experimental results show the effectiveness of our proposition over
traditional IR approaches.
