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|Title:||PRESY: A Context Based Query Reformulation Tool for Information Retrieval on the Web||Authors:||Bouramoul, Abdelkrim
|Affiliations:||Faculty of Information and Communication Technology (ICT)||Keywords:||Computer Science - Information Retrieval; Computer Science - Information Retrieval||Issue Date:||12-Jun-2011||Journal:||Journal of Computer Science (JCS)||Volume:||Vol.6||Issue:||No.4||Start page:||470||End page:||477||Abstract:||
Problem Statement: The huge number of information on the web as well as the
growth of new inexperienced users creates new challenges for information
retrieval. It has become increasingly difficult for these users to find
relevant documents that satisfy their individual needs. Certainly the current
search engines (such as Google, Bing and Yahoo) offer an efficient way to
browse the web content. However, the result quality is highly based on uses
queries which need to be more precise to find relevant documents. This task
still complicated for the majority of inept users who cannot express their
needs with significant words in the query. For that reason, we believe that a
reformulation of the initial user's query can be a good alternative to improve
the information selectivity. This study proposes a novel approach and presents
a prototype system called PRESY (Profile-based REformulation SYstem) for
information retrieval on the web. Approach: It uses an incremental approach to
categorize users by constructing a contextual base. The latter is composed of
two types of context (static and dynamic) obtained using the users' profiles.
The architecture proposed was implemented using .Net environment to perform
queries reformulating tests. Results: The experiments gives at the end of this
article show that the precision of the returned content is effectively
improved. The tests were performed with the most popular searching engine (i.e.
Google, Bind and Yahoo) selected in particular for their high selectivity.
Among the given results, we found that query reformulation improve the first
three results by 10.7% and 11.7% of the next seven returned elements. So as we
can see the reformulation of users' initial queries improves the pertinence of
This work is licensed under a Creative Commons Attribution 4.0 International License.
|Appears in Collections:||Journal Articles|
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