Smart Question-Answering Service for Military Knowledge Graphs Based on Open-Source Intelligence

[Objective] This paper uses open-source intelligence to develop a retrieval-based question-answering service system for military knowledge graphs. [Methods] First, we combined the RoBERTa pre-trained model with data augmentation techniques to address the issues of question classification and named entity recognition in low-resource military question-answering. Then, we proposed a three-dimensional feature entity linking method with the characteristics of military domain entities. Third, we utilized the RoBERTa model and dependency parsing analysis to solve the relationship matching issues for simple and partially complex intents. Finally, we applied heuristic rules to extract the answers. [Results] The F1 scores for question classification and entity recognition reached 99.62% and 98.35%, respectively. The accuracy of relation extraction was 99.72%, and the average accuracy of the question-answering system’s application evaluation reached 91.70%. [Limitations] The military knowledge graph in this question-answering system suffers from low efficiency in automatic expansion, which affects the quality of the question-answering service. [Conclusions] This study developed a military Q&A service with high interpretability and accuracy. © 2024 Chinese Academy of Sciences. All rights reserved.