Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (3): 295-305.doi: 10.11896/jsjkx.240600095

• Artificial Intelligence • Previous Articles     Next Articles

Multi-hop Knowledge Base Question Answering Based on Differentiable Knowledge Graph

WEI Qianqiang1,2,3, ZHAO Shuliang1,2,3, ZHANG Siman4   

  1. 1 College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China
    2 Hebei Provincial Engineering Research Center for Supply China Big Data Analytics & Data Security,Shijiazhuang 050024,China
    3 Hebei Provincial Key Laboratory of Network & Information Security,Shijiazhuang 050024,China
    4 College of Education,Hainan Normal University,Haikou 571158,China
  • Received:2024-06-17 Revised:2024-09-18 Online:2025-03-15 Published:2025-03-07
  • About author:WEI Qianqiang,born in 1997,postgra-duate,is a student member of CCF(No.N8307G).His main research interests include natural language processing and multi-hop knowledge base question answer.
    ZHAO Shuliang,born in 1967,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.62875M).His main research interests include machine lear-ning and intelligent information proces-sing.
  • Supported by:
    National Social Science Foundation of China(18ZDA200),S&T Program of Hebei Pronince(20370301D, 22567606H),Introducing Talents of Studying Overseas Fund of Hebei Pronince(C20230339) and Special Science and Technology Fund of Hebei Normal University(L2023T03).

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Abstract: Knowledge base question answering(KBQA) is a challenging and popular research direction.Currently,embedding-based methods obtain the answer to a question through implicit reasoning and cannot generate complete reasoning paths.Models based on differentiable knowledge graphs only needs the question-answer pairs as weak supervision signals to generate explainable results.An end-to-end encoder-decoder model based on differentiable knowledge graphs is proposed.The encoder uses multi-head attention mechanism and LSTM to model the fine-grained sequence of questions,generating query vectors that can effectively represent the semantic features of each step of the question.The decoder uses feedforward neural networks to effectively represent the weights of each hop in the entire question.Our model solves the problem of information loss caused by previous coarse-grained and non-sequential modeling methods.The experiments are conducted on five datasets:MetaQA-1hop,MetaQA-2hop,MetaQA-3hop,WebQSP and CWQ,and the model achieves accuracy of 97.5%,100%,100%,77.8% and 51.4%,respectively.The ablation experiment shows that each module contributes to the overall performance improvement of the model.

Key words: Knowledge base question answering, Differentiable knowledge graph, Encoder-decoder

CLC Number: 

  • TP391
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