Towards a Possibilistic Information Retrieval System Using Semantic Query Expansion

Abstract

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary “Le Grand Robert”. First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure (possibility and necessity) between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard ‘LeMonde94’ test collection.

A hybrid possibilistic approach for Arabic full morphological disambiguation

“…The combination of the necessity and possibility measures is inspired from the possibilistic Information Retrieval and Knowledge Extraction Systems [32,[37][38][39][40][62][63][64][65]67]. …”

Improving Query Expansion by Automatic Query Disambiguation in Intelligent Information Retrieval

“…For the QE task, they used two components: a co-occurrence based thesaurus built automatically from the documents collection and pseudo relevance feedback by assuming a set of top documents as relevant and injecting representative terms in the original query (Manning et al, 2008). Elayeb et al (2011) and Ben Khiroun et al 2012proposed respectively QE and WSD approaches based on possibilistic networks. However, they did not apply their WSD algorithm on query disambiguation.…”

“…Nevertheless, our implementation of similarity calculus is generic enough to be used with other types of graphs (e.g. dictionary graphs in (Elayeb et al, 2011)).…”

“…One possible solution which can enhance results consists in expanding the context of the query, thus satisfying the user. Query expansion (QE) consists in enriching the user's query by adding new terms to better express his need (Elayeb et al, 2011;Carpineto and Romano, 2012). Another solution arises when one or more terms in query have more than one sense (ambiguous).…”

A Possibilistic Query Translation Approach for Cross-Language Information Retrieval

“…Our experiments are performed through our possibilistic information Retrieval System [10], and implemented using the platform Terrier 2 . It provides many existing matching models such as OKAPI and a new possibilistic matching model proposed by [11].…”