Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (9): 221-227.doi: 10.11896/jsjkx.210700144

• Artificial Intelligence • Previous Articles     Next Articles

Multi-level Inheritance Influence Calculation and Generalization Based on Knowledge Graph

KONG Shi-ming1, FENG Yong2, ZHANG Jia-yun3   

  1. 1 College of Artificial Intelligence,Chongqing University of Arts and Sciences,Chongqing 402160,China
    2 College of Computer Science,Chongqing University,Chongqing 400044,China
    3 China Certification & Inspection Group Chongqing Co., Ltd.,Chongqing 401120,China
  • Received:2021-07-13 Revised:2021-10-19 Online:2022-09-15 Published:2022-09-09
  • About author:KONG Shi-ming,born in 1972,master,lecturer,is a member of China Compu-ter Federation.His main research in-terests include social network analysis,knowledge graph and machine learning.
    FENG Yong,born in 1977,Ph.D,professor,Ph.D supervisor, is a member of China Computer Federation.His main research interests include big data ana-lysis and data mining,artificial intelligence and big data processing,deep learning.
  • Supported by:
    Zhejiang Lab(2021KE0AB01),Guangxi Key Laboratory of Trusted Software(kx202006),Chongqing Talent Plan Innovation and Entrepreneurship Demonstration Team(CQYC201903167),Chongqing Technology Innovation and Application Development Special General Project(cstc2020jscx-sbqwX0015) and Major Industrial Technology R & D Projects of Chongqing High Tech Industry(2018148208).

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Abstract: Influence calculation and analysis are widely used in social networks,web page importance evaluation and other fields.There is still a lack of effective and universal solution for the multi-level influence calculation with inheritance chain and time span factors.At the same time,the calculation of maximizing the propagation influence is an NP hard problem,whose approximate algorithm has low accuracy and complicated computation.In order to solve the above problems,this paper proposes a multi-level inheritance influence and generalization algorithm based on knowledge graph to realize the calculation of inheritance influence and inheritance relationship.The algorithm uses the breadth first search hierarchy computing model of knowledge graph,and takes into account the time span constraints to calculate the inheritance influence and inheritance chain.In order to optimize the computational efficiency,the strategy of depth first search and different levels with different weights is further used to only calculate the influence of the top n levels.The above method can not only calculate the inheritance influence and inheritance chain well,but also can be generalized into various communication influence calculation models.On this basis,this paper proposes a local optimal search similarity algorithm to maximize the propagation influence by selecting the nodes with large propagation influence as spare nodes.It achieves competitive results in running speed and the maximum number of propagation nodes.Finally,the effectiveness of the proposed method is verified by a variety of simulation experiments.

Key words: Calculation of inheritance influence, Inheritance chain calculation, Knowledge graph, Maximization of propagationinfluence

CLC Number: 

  • TP391
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