Authors:
Jones Avelino
1
;
2
;
Giselle Rosa
1
;
Gustavo Danon
1
;
Kelli Cordeiro
3
and
Maria C. Cavalcanti
1
Affiliations:
1
Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ, Brazil
;
2
Centro de Análise de Sistemas Navais (CASNAV), Rio de Janeiro, RJ, Brazil
;
3
Subchefia de Comando e Controle (SC-1), Ministério da Defesa, Brasília, DF, Brazil
Keyword(s):
Named Entity Recognition, Relation Extraction, Knowledge Graph, Command and Control.
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.
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