Paper Info
Title
Implication-based and cardinality-based inclusions in information retrieval
Abstract
This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. Two kinds of graded inclusions are considered. 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
10.1109/FUZZY.2009.5277249
Jeju Island
Keywords
Field
DocType
fuzzy logic,fuzzy set theory,information retrieval,cardinality-based inclusions,fuzzy logic,fuzzy operations,fuzzy sets,implication-based inclusions,information retrieval process
Boolean function,Data mining,Data modeling,Information retrieval,Computer science,Fuzzy logic,Cardinality,Theoretical computer science,Fuzzy set,Optical computing
Conference
ISSN
ISBN
Citations 
1098-7584 E-ISBN : 978-1-4244-3597-5
978-1-4244-3597-5
2
PageRank 
References 
Authors
0.38
16
4
Name
Order
Citations
PageRank
Patrick Bosc160175.23
Laurent Ughetto21018.97
Olivier Pivert3891101.81
Vincent Claveau416232.15