CG2A: Conceptual Graphs Generation Algorithm

TL;DR

This paper introduces CG2A, an algorithm that efficiently generates synthetic Conceptual Graphs by leveraging their full expressivity, aiding in testing and validating knowledge representation algorithms.

Contribution

The paper presents CG2A, a novel algorithm that constructs synthetic CG databases using constraints and ontological knowledge, enhancing variability and expressivity.

Findings

CG2A can generate diverse, expressive CG databases.

The algorithm automates vocabulary and $\gamma$-CGs generation.

Generated CGs are suitable for testing knowledge algorithms.

Abstract

Conceptual Graphs (CGs) are a formalism to represent knowledge. However producing a CG database is complex. To the best of our knowledge, existing methods do not fully use the expressivity of CGs. It is particularly troublesome as it is necessary to have CG databases to test and validate algorithms running on CGs. This paper proposes CG2A, an algorithm to build synthetic CGs exploiting most of their expressivity. CG2A takes as input constraints that constitute ontological knowledge including a vocabulary and a set of CGs with some label variables, called $\gamma$-CGs, as components of the generated CGs. Extensions also enable the automatic generation of the set of $\gamma$-CGs and vocabulary to ease the database generation and increase variability.