Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240800047-6.doi: 10.11896/jsjkx.240800047

• Artificial Intelligence • Previous Articles     Next Articles

Concept Cognition for Knowledge Graphs by Mining Double Granularity Concept Characteristics

HU Xin, DUAN Jiangli, HUANG Denan   

  1. College of Big Data and Intelligent Engineering,Yangtze Normal University,Chongqing 408100,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:HU Xin,born in 1988. Ph.D,associate professor,master supervisor,is a CCF member(No.23780M). His main research interests include question answering over knowledge graph,concept cognition,granular computing,and data mining.

    DUAN Jiangli,born in 1989, Ph.D,lecturer. Her main research interests include granular computing,concept cognition,knowledge graph,data mining and question answering.
  • Supported by:
    Chongqing Social Science Planning Project(2024BS034),National Natural Science Foundation of China(62106024),China Postdoctoral Science Foundation(2022M711458),Chongqing Language Research Project(yyk22105),Special Funding for Postdoctoral Research Projects in Chongqing(2023CQBSHTB3118) and Project of Chongqing Education Commission of China(KJZD-K202401405,KJQN202401432,KJQN202201410,KJQN202301415,KJQN202301416).

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Abstract: Existing natural language understanding methods are based on information retrieval and matching,which don’t have cognitive ability like humans. To simulate human cognitive ability to concepts,in this paper,the main task of concept cognition for knowledge graphs is to mine double-granularity concept characteristics,frequent attributes and attribute values of concept,from two granularities,i.e.,the existence or nonexistence of attributes and the attribute value,which enables machines to distinguish or cognize concepts. Firstly,an algorithm is proposed to mine double-granularity concept characteristics from concept-relatedinformation in the knowledge graph. Secondly,to promote synergy between two granularities,the monotonicity of double-granularity attribute pattern is proposed and proven. Thirdly,to unleash the value of above monotonicity and accelerate the mining process,the representativeness of the maximal frequent attribute pattern is used. Finally,experiments verify the efficiency of the mining algorithm,the monotonicity of double-granularity attribute patterns,the representativeness of maximal frequent attribute pattern,and the cognitive ability of double-granularity concept characteristics.

Key words: Knowledge graph, Data mining, Granular computing, Concept cognition

CLC Number: 

  • TP18
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