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Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing

Sébastien Ferré 1 Marianne Huchard 2 Mehdi Kaytoue 3 Sergei Kuznetsov 4 Amedeo Napoli 5 
1 SemLIS - Semantics, Logics, Information Systems for Data-User Interaction
2 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this chapter, we introduce Formal Concept Analysis (FCA) and some of its extensions. FCA is a formalism based on lattice theory aimed at data analysis and knowledge processing. FCA allows the design of so-called concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge engineering and the design of knowledge-based systems. Indeed, FCA is closely related to knowledge discovery in databases, knowledge representation and reasoning. Accordingly, FCA supports a wide range of complex and intelligent tasks among which classification, information retrieval, recommendation, network analysis, software engineering and data management. Finally, FCA is used in many applications demonstrating its growing importance in data and knowledge sciences.
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Contributor : Marianne Huchard Connect in order to contact the contributor
Submitted on : Monday, December 21, 2020 - 10:20:56 PM
Last modification on : Tuesday, October 25, 2022 - 4:17:45 PM



Sébastien Ferré, Marianne Huchard, Mehdi Kaytoue, Sergei Kuznetsov, Amedeo Napoli. Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing. Pierre Marquis; Odile Papini; Henri Prade. A Guided Tour of Artificial Intelligence Research, 2, Springer International Publishing, pp.411-445, 2020, AI Algorithms, 978-3-030-06167-8. ⟨10.1007/978-3-030-06167-8_13⟩. ⟨hal-03085699⟩



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