Knowledge Graph Generation from Text Using Supervised Approach Supported by a Relation Metamodel: An Application in C2 Domain

Jones Avelino, Jones Avelino, Giselle Rosa, Gustavo Danon, Kelli Cordeiro, Maria C. Cavalcanti

2024

Abstract

In the military domain of Command and Control (C2), doctrines contain information about fundamental concepts, rules, and guidelines for the employment of resources in operations. One alternative to speed up personnel (workforce) preparation is to structure the information of doctrines as knowledge graphs (KG). However, the scarcity of corpora and the lack of language models (LM) trained in the C2 domain, especially in Portuguese, make it challenging to structure information in this domain. This article proposes IDEA-C2, a supervised approach for KG generation supported by a metamodel that abstracts the entities and relations expressed in C2 doctrines. It includes a pre-annotation task that applies rules to the doctrines to enhance LM training. The IDEA-C2 experiments showed promising results in training NER and RE tasks, achieving over 80% precision and 98% recall, from a C2 corpus. Finally, it shows the feasibility of exploring C2 doctrinal concepts through an RDF graph, as a way of improving the preparation of military personnel and reducing the doctrinal learning curve.

Paper Citation

in Harvard Style

Avelino J., Rosa G., Danon G., Cordeiro K. and C. Cavalcanti M. (2024). Knowledge Graph Generation from Text Using Supervised Approach Supported by a Relation Metamodel: An Application in C2 Domain. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 281-288. DOI: 10.5220/0012629300003690

in Bibtex Style

@conference{iceis24,
author={Jones Avelino and Giselle Rosa and Gustavo Danon and Kelli Cordeiro and Maria C. Cavalcanti},
title={Knowledge Graph Generation from Text Using Supervised Approach Supported by a Relation Metamodel: An Application in C2 Domain},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012629300003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Knowledge Graph Generation from Text Using Supervised Approach Supported by a Relation Metamodel: An Application in C2 Domain
SN - 978-989-758-692-7
AU - Avelino J.
AU - Rosa G.
AU - Danon G.
AU - Cordeiro K.
AU - C. Cavalcanti M.
PY - 2024
SP - 281
EP - 288
DO - 10.5220/0012629300003690
PB - SciTePress