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Mining Description Logics Concepts With Relational Concept Analysis

Marianne Huchard 1 Amedeo Napoli 2 Amine Mohamed Rouane Hacene 2 Petko Valtchev 3
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Symbolic objects were originally intended to bring both more structure in data and more intelligibility in final results to statistical data analysis. We present here a framework of similar motivation, i.e., combining a data analysis method, —the concept analysis (fca) — with a knowledge description language inspired by description logic (dl) formalism. The focus is hence on proper handling of relations between individuals in the construction of formal concepts. We illustrate the relational concept analysis (rca) framework which complements standard fca with a dedicated data format, a set of scaling operators, an iterative process for lattice construction, and translations to and from a dl language.
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Contributor : Marianne Huchard <>
Submitted on : Monday, October 29, 2007 - 7:28:02 PM
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Marianne Huchard, Amedeo Napoli, Amine Mohamed Rouane Hacene, Petko Valtchev. Mining Description Logics Concepts With Relational Concept Analysis. Paula Brito, Guy Cucumel, Patrice Bertrand and Francisco de Carvalho. Selected Contributions in Data Analysis and Classification, Springer Berlin Heidelberg, pp.259-270, 2007, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-540-73558-8 (Print) 978-3-540-73560-1 (Online). ⟨10.1007/978-3-540-73560-1⟩. ⟨lirmm-00183384⟩



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