Authors:
Ounas Asfari
1
;
Bich-Liên Doan
1
;
Yolaine Bourda
1
and
Jean-Paul Sansonnet
2
Affiliations:
1
SUPELEC, France
;
2
LIMSI-CNRS and University of Paris 11, France
Keyword(s):
Query Reformulation, Context, Task modeling, Information Retrieval, Personalization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Access to relevant information adapted to the needs and the context of the user is a real challenge in the Web Search, owing to the increase of heterogeneous resources on the web. In most of cases, user queries are shortened and ambiguous, thus we need to handle implicit needs or intentions that are behind these queries. For improving user query processing, we present a context-based method for query expansion that automatically generates context-related terms. Here, we consider the user context as the current state of the task that the user is undertaking when the information retrieval process takes place, thus State Reformulated Queries (SRQ) are generated according to the user task state and the ontological user profile to provide personalized results in a particular context.