Paper Info
Title
Graded-Inclusion-Based Information Retrieval Systems
Abstract
This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. In this framework, documents and queries are represented by fuzzy sets, which are paired with operations like fuzzy implications and T-norms. Through different experiments, it is shown that only some among the wide range of fuzzy operations are relevant for information retrieval. When appropriate settings are chosen, it is possible to mimic classical systems, thus yielding results rivaling those of state-of-the-art systems. These positive results validate the proposed approach, while negative ones give some insights on the properties needed by such a model. Moreover, this paper shows the added-value of this graded inclusion-based model, which gives new and theoretically grounded ways for a user to easily weight his query terms, to include negative information in his queries, or to expand them with related terms.
Year
DOI
Venue
2009
10.1007/978-3-642-00958-7_24
ECIR
Keywords
Field
DocType
fuzzy operation,negative information,fuzzy set,graded inclusion,information retrieval,graded inclusion-based model,fuzzy implication,appropriate setting,information retrieval process,graded-inclusion-based information retrieval systems,fuzzy logic mechanism,information retrieval system,fuzzy logic
Data mining,Neuro-fuzzy,Fuzzy classification,Information retrieval,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Fuzzy set,Fuzzy number,Fuzzy associative matrix
Conference
Volume
ISSN
Citations 
5478
0302-9743
6
PageRank 
References 
Authors
0.54
15
4
Name
Order
Citations
PageRank
Patrick Bosc160175.23
Vincent Claveau216232.15
Olivier Pivert3891101.81
Laurent Ughetto41018.97