EvolveKG: a general framework to learn evolving knowledge graphs
文献类型：期刊论文
| 作者 | Liu, Jiaqi1; Yu, Zhiwen1; Guo, Bin1; Deng, Cheng2; Fu, Luoyi2; Wang, Xinbing2; Zhou, Chenghu3 |
| 刊名 | FRONTIERS OF COMPUTER SCIENCE
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 18期号:3页码:17 |
| 关键词 | knowledge graph evolution modal characterization algorithmic implementation |
| ISSN号 | 2095-2228 |
| DOI | 10.1007/s11704-022-2467-9 |
| 通讯作者 | Yu, Zhiwen(zhiwenyu@nwpu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | SCALE ; HABIT |
| 资助项目 | National Key R&D Program of China[2021ZD0113305] ; National Natural Science Foundation of China[61960206008] ; National Natural Science Foundation of China[62002292] ; National Natural Science Foundation of China[42050105] ; National Natural Science Foundation of China[62020106005] ; National Natural Science Foundation of China[62061146002] ; National Natural Science Foundation of China[61960206002] ; National Science Fund for Distinguished Young Scholars[61725205] ; Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001152903800003 |
| 出版者 | HIGHER EDUCATION PRESS |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; National Science Fund for Distinguished Young Scholars ; Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202603]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yu, Zhiwen |
| 作者单位 | 1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Peoples R China 2.Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100864, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jiaqi,Yu, Zhiwen,Guo, Bin,et al. EvolveKG: a general framework to learn evolving knowledge graphs[J]. FRONTIERS OF COMPUTER SCIENCE,2024,18(3):17. |
| APA | Liu, Jiaqi.,Yu, Zhiwen.,Guo, Bin.,Deng, Cheng.,Fu, Luoyi.,...&Zhou, Chenghu.(2024).EvolveKG: a general framework to learn evolving knowledge graphs.FRONTIERS OF COMPUTER SCIENCE,18(3),17. |
| MLA | Liu, Jiaqi,et al."EvolveKG: a general framework to learn evolving knowledge graphs".FRONTIERS OF COMPUTER SCIENCE 18.3(2024):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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