Computer Science ›› 2022, Vol. 49 ›› Issue (5): 194-199.doi: 10.11896/jsjkx.210400195

• Artificial Intelligence • Previous Articles     Next Articles

Academic Knowledge Graph-based Research for Auxiliary Innovation Technology

ZHONG Jiang1, YIN Hong1, ZHANG Jian2   

  1. 1 College of Computer Science,Chongqing University,Chongqing 400044,China
    2 Chongqing Xixintianyuan Data Information Co.,Ltd.,Chongqing 401121,China
  • Received:2021-04-19 Revised:2021-09-08 Online:2022-05-15 Published:2022-05-06
  • About author:ZHONG Jiang,born in 1974,Ph.D,professor.His main research interests include natural language processing,big data analysis and mining,cloud and network integration technology.
  • Supported by:
    Major Project of Chongqing Higher Education Teaching Reform Research(191003) and New Engineering Research and Practice Project of the Ministry of Education(E-JSJRJ20201335).

Abstract: Due to the rapid updating of computer knowledge with many ambiguities,it is difficult for students to seek reasonable solutions for independent innovation.As an auxiliary innovation tool,intelligent question-answering system can help students to grasp the frontier of subject development,find out solutions for problems faster and precisely.In this paper,a knowledge graph of scientific research is constructed based on a large-scale database of scientific and technological documents,which realizes an intelligent question answering system for assisting students in innovation.In order toreduce the influence of noisy entities on query questions,this paper proposes an auxiliary task enhanced intent information for question answering in computer domain(ATEI-QA).Compared with the traditional method,this method can extract the question intention information more accurately and further reduce the influence of noisy entity on intention recognition.Additionally,we conduct a series of experimental studies on computer and common datasets,and compare with three mainstream methods.Finally,experimental results demonstrate that our model achieves significant improvements against with three baselines,improving MAP and MRR scores by average of 3.27%,1.72% in the computer dataset and 4.37%,2.81% in the common dataset respectively.

Key words: Question answering, Knowledge graph, Intent recognition, Deep learning

CLC Number: 

  • TP391
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