CG2A: Conceptual Graphs Generation Algorithm

DOI

10.2991/asum.k.210827.009How to use a DOI?

Keywords

Conceptual Graphs, Data generation, Predictability, Variability

Abstract

Conceptual Graphs (CGs) are a formalism to represent knowledge. The production of CG benchmarks is currently a crucial need in the community to validate algorithms. 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 γ-CGs, as components of the generated CGs. Extensions also enable the automatic generation of the set of γ-CGs and vocabulary to ease the database generation and increase variability.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Adam Faci
AU  - Marie-Jeanne Lesot
AU  - Claire Laudy
PY  - 2021
DA  - 2021/08/30
TI  - CG2A: Conceptual Graphs Generation Algorithm
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 63
EP  - 70
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.009
DO  - 10.2991/asum.k.210827.009
ID  - Faci2021
ER  -