  Despite limited success, information retrieval (IR) systems today are not
intelligent or reliable. IR systems return poor search results when users
formulate their information needs into incomplete or ambiguous queries (i.e.,
weak queries). Therefore, one of the main challenges in modern IR research is
to provide consistent results across all queries by improving the performance
on weak queries. However, existing IR approaches such as query expansion are
not overly effective because they make little effort to analyze and exploit the
meanings of the queries. Furthermore, word sense disambiguation approaches,
which rely on textual context, are ineffective against weak queries that are
typically short. Motivated by the demand for a robust IR system that can
consistently provide highly accurate results, the proposed study implemented a
novel topic detection that leveraged both the language model and structural
knowledge of Wikipedia and systematically evaluated the effect of query
disambiguation and topic-based retrieval approaches on TREC collections. The
results not only confirm the effectiveness of the proposed topic detection and
topic-based retrieval approaches but also demonstrate that query disambiguation
does not improve IR as expected.
