Premium
Asymmetric Composition of Possibilistic Operators in Formal Concept Analysis: Application to the Extraction of Attribute Implications from Incomplete Contexts
Author(s) -
AitYakoub Zina,
Djouadi Yassine,
Dubois Didier,
Prade Henri
Publication year - 2017
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21900
Subject(s) - formal concept analysis , galois connection , complement (music) , computer science , composition (language) , operator (biology) , formal methods , complete information , theoretical computer science , possibility theory , mathematics , artificial intelligence , fuzzy logic , algorithm , programming language , fuzzy set , discrete mathematics , mathematical economics , linguistics , biochemistry , chemistry , philosophy , gene , phenotype , repressor , complementation , transcription factor
Abstract Formal concept analysis theory (FCA) classically relies on the use of the Galois powerset operator. Formal similarities between possibility theory and formal concept analysis have led to the use of possibilistic operators in FCA, which were ignored before. In this paper, an approach based on the use of asymmetric composition of the two most usual possibilistic operators is proposed. It enables us to complement the stem base, by deriving attribute implications with disjunctions on both sides of the implications. Besides, the approach is also generalized to incomplete contexts involving explicit positive and negative information. We outline the potential application of these results to the completion of TBoxes in description logic.