计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240800047-6.doi: 10.11896/jsjkx.240800047

• 人工智能 • 上一篇    下一篇

从挖掘双粒度概念特征的角度实现知识图谱概念认知

胡新, 段江丽, 黄德楠   

  1. 长江师范学院大数据与智能工程学院 重庆 408100
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 段江丽(duanjl@yznu.edu.cn)
  • 作者简介:(huxin@yznu.edu.cn)
  • 基金资助:
    重庆市社会科学规划项目(2024BS034);国家自然科学基金(62106024);中国博士后科学基金(2022M711458);重庆市语言文字重点项目(yyk22105);重庆市博士后研究项目特别资助(2023CQBSHTB3118);重庆市教委科技项目(KJZD-K202401405,KJQN202401432,KJQN202201410,KJQN202301415,KJQN202301416)

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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摘要： 现有的自然语言理解方法是基于信息检索和匹配的,不像人类那样具有认知能力。为了模拟人类对概念的认知能力,知识图谱概念认知的主要任务是从属性有无和属性值两个粒度挖掘概念特征,即概念的频繁属性和属性值,使机器能够区分或认知概念。首先,提出了一种从知识图谱中的概念相关信息中挖掘双粒度概念特征的算法。其次,提出了双粒度属性模式的单调性,以促进两个粒度之间的协同作用并加快挖掘过程。接着,利用极大频繁属性模式的代表性来释放上述单调性的值,加速挖掘过程。最后,实验验证了算法的有效性、双粒度属性模式的单调性、极大频繁模式的代表性和双粒度概念特征的认知能力。

关键词: 知识图谱, 数据挖掘, 粒计算, 概念认知

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

中图分类号: 

  • TP18
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