首页|EvolveKG:a general framework to learn evolving knowledge graphs

EvolveKG:a general framework to learn evolving knowledge graphs

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG-a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph-a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

knowledge graphevolutionmodal characteriza-tionalgorithmic implementation

Jiaqi LIU、Zhiwen YU、Bin GUO、Cheng DENG、Luoyi FU、Xinbing WANG、Chenghu ZHOU

展开 >

School of Computer Science,Northwestern Polytechnical University,Xi'an 710129,China

Department of Computer Science,Shanghai Jiao Tong University,Shanghai 200240,China

Institute of Geographical Science and Natural Resources Research,Chinese Academy of Sciences,Beijing 100864,China

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Science Fund for Distinguished Young ScholarsShanghai Pilot Program for Basic Research-Shanghai Jiao Tong University

2021ZD011330561960206008620022924205010562020106005620611460026196020600261725205

2024

10.1007/s11704-022-2467-9

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2024.18(3)
  • 42