Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (9): 202-207.doi: 10.11896/jsjkx.220300277

• Artificial Intelligence • Previous Articles     Next Articles

Key-Value Relational Memory Networks for Question Answering over Knowledge Graph

RAO Zhi-shuang1, JIA Zhen1, ZHANG Fan1,2, LI Tian-rui1,2,3   

  1. 1 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    2 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province,Chengdu 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2022-03-30 Revised:2022-06-08 Online:2022-09-15 Published:2022-09-09
  • About author:RAO Zhi-shuang,born in 1996,postgraduate.His main research interests include natural language process and knowledge based question answering.
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets and granular computing.
  • Supported by:
    National Natural Science Foundation of China(62176221).

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Abstract: Question answering over knowledge graph(KG-QA) systems map the natural language question to the 〈subject,predicate,object〉 triple in the knowledge graph(KG) by semantic analysis of the given question,and infer the triple to get the answer of the question.Due to the diversity of natural languages,a question may be expressed in multiple forms but the triples in KGs are structured data in a standard form.It is challenging to map questions to triples in KGs.This paper proposes a novel Key-Value relational memory network,starting from the perspective of KGs,and focusing on the relationship between the candidate answer knowledge and the relationship between the knowledge in KGs and the question representations.In addition,the attention mechanism is applied in the proposed model,so that it has better interpretability than other baseline models.We evaluate the method on WebQuestions benchmark.Experiment results show that,compared with the best methods based on information extraction,the F1 value of the proposed method increases by 5.9% and is slightly higher than that of the optimal methods based on semantic analysis,which verifies the effectiveness of the proposed method.

Key words: Question answering over knowledge graph, Knowledge graph, Relational memory networks, Attention mechanism, Deep learning

CLC Number: 

  • TP391
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