Interval-Valued Fuzzy Extension of Formal Concept Analysis for Information Retrieval

Abstract

Abstract. In this paper, we propose an information retrieval approach based on interval-valued fuzzy theory which allows to express the total and partial ignorance in the knowledge domain (i.e the incidence matrix). Hence, we propose i ) to deal with the presence of partial or total ignorance in the knowledge domain; ii ) the application of different interval-valued fuzzy generalized derivations operators to express different queries forms, namely conjunctive and disjunctive queries as well as negation in the queries; iii ) the linearization of the lattice structure of formal concepts satisfying a given query based on hamming distance in order to enhance lattice navigation.

A comprehensive survey on formal concept analysis, its research trends and applications

“…3. This extension has been successfully applied in information retrieval and the rule mining tasks (Zerarga and Djouadi, 2013;Zhai et al, 2012).…”

“…Other domains discuss the mathematics behind FCA with a fuzzy setting, an interval-valued fuzzy setting, possibility theory, a rough setting, a triadic, factor and incomplete context to apply these extensions in the appropriate context for knowledge processing tasks. Ferjani et al, 2012Feature extractions Formica, 2012 Semantic web Galitsky et al, 2013 Finding patterns on parse thickets Hamrouni et al, 2013 Finding some frequent itemset Li and Guo, 2013 Investigating formal query De Maio et al, 2014 Text mining Muangprathub et al, 2013 Classification Li andTsai, 2013 Text mining Finding cousins Neznanov and Kuznetsov, 2013 FCART tool Poshyvanyk et al, 2012 Concept location Priss, 2006 Application in information sciences Senatore and Pasi, 2013 Finding correlations Li and Tsai, 2013 Opinion classification Zerarga and Djouadi, 2013 Information retrieval Military intelligence Du and Hai, 2013 Mining web page Elzinga et al, 2010 Terrorist threat assessment Poelmans et al, 2013c Criminal trajectories Priss, 2011 Unix system monitoring Romanov et al, 2012 Detect anomalies Web services…”