Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 302-313.doi: 10.11896/jsjkx.221000170

• Artificial Intelligence • Previous Articles     Next Articles

DL+:An Enhanced Double-layer Framework for Knowledge Graph Reasoning

WU Yuejia, ZHOU Jiantao   

  1. College of Computer Science(College of Software),Inner Mongolia University,Hohhot 010021,China
    Engineering Research Center of Ecological Big Data,Ministry of Education,Hohhot 010021,China
    National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian,Hohhot 010021,China
    Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software,Hohhot 010021,China
    Inner Mongolia Key Laboratory of Social Computing and Data Processing,Hohhot 010021,China
    Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data,Hohhot 010021,China
    Inner Mongolia Engineering Laboratory for Big Data Analysis Technology,Hohhot 010021,China
  • Received:2022-10-23 Revised:2023-03-06 Online:2023-12-15 Published:2023-12-07
  • About author:WU Yuejia,born in 1996,Ph.D candidate,is a student member of China Computer Federation.Her main research interests include knowledge graph,knowledge graph representing and knowledge graph reasoning.
    ZHOU Jiantao,born in 1974,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include cloud computing technology,social network recommendation technology,software engineering and so on.
  • Supported by:
    National Natural Science Foundation of China(62162046),Inner Mongolia Science and Technology Project(2021GG0155),Natural Science Foundation of Major Research Plan of Inner Mongolia(2019ZD15),Natural Science Foundation of Inner Mongolia,China(2019GG372) and Special Fund for Graduate Innovation and Entrepreneurship of Inner Mongolia University(11200-5223737).

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Abstract: As an important research field of the graph database,the knowledge graph(KG) can formally describe things and their relationships in the real world.However,its incompleteness and sparsity hinder its application in many fields.The knowledge graph reasoning(KGR) technology aims to complete the knowledge graph by inferring new knowledge or identifying wrong knowledge according to the existing knowledge in the knowledge graph.Although existing reasoning methods can obtain partially effective knowledge paths,there are still some problems such as incomplete acquisition paths,ignoring local information,introducing noise.Based on this,this paper finds and explicitly proposes the problem of poor path connectivity,proves that the reasoning validity is positively correlated with the path connectivity ratio between entities,and further proposes a double-layer framework DL+ which is used to enhance the performance of existing reasoning methods.The first layer is a knowledge augmenter,which mainly uses the community discovery algorithm to extract the entity neighborhood information on the initial KG and build new knowledge to expand the knowledge scale,and then designs a community pruning optimization method to remove the noise introduced in the construction.Finally,the augmented KG is extracted and restored to the same structure as the initial KG representation and output to the second layer to ensure the “plug-and-play” feature of the model.The second layer is a knowledge reasoner,which can enhance the existing KGR model by learning and reasoning on the KG after knowledge augmentation,so that the model can obtain better reasoning results when the graph path connectivity ratio is high.Finally,a large number of experimental results on four standard KG datasets show that the DL+ can effectively alleviate the problem of poor path connectivity between entities,and improve the average prediction accuracy by 4.798% compare with nine types of benchmark methods.

Key words: Knowledge graph, Knowledge graph reasoning, Community discovery, Path connectivity, Link prediction

CLC Number: 

  • TP391
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