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cgSpan: Pattern Mining in Conceptual Graphs

Abstract : Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It extends the DMGM-GSM algorithm that takes taxonomy-based labeled graphs as input; it includes three more kinds of knowledge of the CG formalism: (a) the fixed arity of relation nodes, handling graphs of neighborhoods centered on relations rather than graphs of nodes, (b) the signatures, avoiding patterns with concept types more general than the maximal types specified in signatures and (c) the inference rules, applying them during the pattern mining process. The experimental study highlights that cgSpan is a functional CG Frequent Pattern Mining algorithm and that including CGs specificities results in a faster algorithm with more expressive results and less redundancy with vocabulary.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03400687
Contributor : Adam Faci Connect in order to contact the contributor
Submitted on : Monday, October 25, 2021 - 10:52:39 AM
Last modification on : Monday, December 6, 2021 - 5:12:03 PM

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Adam Faci, Marie-Jeanne Lesot, Claire Laudy. cgSpan: Pattern Mining in Conceptual Graphs. Int. Conf. on Artificial Intelligence and Soft Computing (ICAISC2021), Jun 2021, Zakopane, Poland. pp.149-158, ⟨10.1007/978-3-030-87897-9_14⟩. ⟨lirmm-03400687⟩

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