cgSpan: Pattern Mining in Conceptual Graphs

• URL: http://arxiv.org/abs/2110.15058v1
• Date: Tue, 26 Oct 2021 14:28:06 GMT
• Title: cgSpan: Pattern Mining in Conceptual Graphs
• Authors: Adam Faci (LFI, TRT), Marie-Jeanne Lesot (LFI), Claire Laudy (TRT)
• Abstract summary: Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It includes three more kinds of knowledge of the CG formalism: (a) the fixed arity of relation 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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