cgSpan: Pattern Mining in Conceptual Graphs
  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.
