Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11): 227-233.doi: 10.11896/jsjkx.220900206

• Artificial Intelligence • Previous Articles     Next Articles

Deep Hashing-based Retrieval Framework for KBQA

LIU Shuo1 , ZHOU Gang1,2, LI Zhufeng1, WU Hao1   

  1. 1 College of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2022-09-22 Revised:2023-01-03 Online:2023-11-15 Published:2023-11-06
  • About author:LIU Shuo,born in 1998,postgraduate.His main research interests include knowledge graph and data mining.ZHOU Gang,born in 1974,Ph.D,professor.His main research interests include knowledge graph,mass data processing and knowledge graph.
  • Supported by:
    Key Science and Technology Program of Henan Province,China(222102210081).

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Abstract: Question answering over knowledge base usually involves three sub-tasks,topic entity recognition,entity linking and relation detection.Given that the knowledge base usually contains enormous entities and relationships,previous approaches prefer to utilize sophisticated rules and inverted index to retrieve candidate items.In this paper,a new approach is proposed to construct a retrieval framework for question answering over knowledge base to address the problems of search space limitations,low recall and the difficulty to incorporate semantic information demonstrated by previous approach.The framework consists of text retrieve module and hash retrieve module.A cascade retrieve model which contains traditional text retrieve and hash retrieve(semantic information remained) is constructed by recalling twice.The experiment,utilizing the datasets provided by KgCLUE and NLPCC2016,demonstrates that this deep hashing-based retrieve framework can acquire high-quality candidates efficiently and access the knowledge base easily with limited time cost.

Key words: Retrieval framework, Knowledge based question answering, Deep hashing learning

CLC Number: 

  • TP391
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