Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (1): 87-93.doi: 10.11896/jsjkx.240900064

• Technology Research and Application of Large Language Model • Previous Articles     Next Articles

Retrieval-augmented Generative Intelligence Question Answering Technology Based on Knowledge Graph

CHENG Zhiyu1, CHEN Xinglin2, WANG Jing3, ZHOU Zhongyuan4, ZHANG Zhizheng5,6   

  1. 1 Joint Graduate School,Southeast University,Suzhou,Jiangsu 215000,China
    2 College of Software Engineering,Southeast University,Nanjing 211189,China
    3 Science and Technology on Information Systems Engineering Laboratory,Nanjing 210023,China
    4 The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210023,China
    5 School of Computer Science and Engineering,Southeast University,Nanjing 211189,China
    6 Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University),Ministry of Education,Nanjing 211189,China
  • Received:2024-09-10 Revised:2024-10-20 Online:2025-01-15 Published:2025-01-09
  • About author:CHENG Zhiyu,born in 1999,postgra-duate.His main research interests include knowledge graph and question answering systems.
    ZHANG Zhizheng,born in 1980,Ph.D,associate professor,is a member of CCF(No.32012M).His main research interests include knowledge representation and reasoning,and knowledge agents.
  • Supported by:
    Pre-research Key Laboratory Fund for Equipment(6142101210205) and National Defense Science and Technology Innovation Special Zone Funding Project.

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Abstract: A knowledge graph-based retrieval-augmented generation framework is proposed to achieve military intelligence question answering.The framework effectively acquires background knowledge through question classification,entity recognition,entity linking,and knowledge retrieval.Considering the multi-constraint characteristics of intelligence questions,answer set programming is used to reduce the amount of knowledge through constraints or to directly obtain answers.Finally,a large language model solves the questions based on the refined knowledge,minimizing attribute recognition and linking issues during question understanding.Experiments on the MilRE dataset demonstrate that the framework provides enhanced knowledge retrieval capabilities based on knowledge graphs and offers superior performance in answering military intelligence questions.

Key words: Intelligence question-answering, Answer set programming, Large language models, Retrieval-augmented generation, Knowledge graph

CLC Number: 

  • TP391
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