cgSpan: Pattern Mining in Conceptual Graphs

TL;DR

cgSpan is a novel algorithm for mining frequent patterns in Conceptual Graphs, incorporating CG-specific features like relation arity, signatures, and inference rules, leading to more efficient and expressive pattern discovery.

Contribution

It extends existing graph mining algorithms to handle the unique aspects of Conceptual Graphs, improving speed and expressiveness in pattern mining.

Findings

cgSpan is effective for CG pattern mining

Including CG-specific features enhances performance

The algorithm produces less redundant and more expressive patterns

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.