Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement

doi:10.1109/TKDE.2024.3390683

	Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement
	Zhang, Fuwei 1; Zhang, Zhao 2; Zhuang, Fuzhen 3,4; Zhao, Yu 5; Wang, Deqing 6; Zheng, Hongwei 7
	2024-11-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	36 期号:11 页码:7115-7128
摘要	Temporal Knowledge Graph (TKG) reasoning involves predicting future facts based on historical information by learning correlations between entities and relations. Recently, many models have been proposed for the TKG reasoning task. However, most existing models cannot efficiently utilize historical information, which can be summarized in two aspects: 1) Many models only consider the historical information in a fixed time range, resulting in a lack of useful information; 2) some models use all the historical facts, thus some noise or invalid facts are introduced during reasoning. In this regard, we propose a novel TKG reasoning model with dynamic memory enhancement (DyMemR). Inspired by human memory, we introduce memory capacity, memory loss, and repetition stimulation to design a human-like memory pool that could remember potentially useful historical facts. To fully leverage the memory pool, we utilize a two-stage training strategy. The first stage is guided by the memory-based encoding module which learns embeddings from memory-based subgraphs generated through the memory pool. The second stage is the memory-based scoring module that emphasizes the historical facts in the memory pool. Finally, we extensively validate the superiority of DyMemR against various state-of-the-art baselines.
关键词	Cognition Knowledge graphs Task analysis History Convolution Biological system modeling Semantics Temporal knowledge graph (TKG) memory pool temporal knowledge graph reasoning
DOI	10.1109/TKDE.2024.3390683
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021ZD0113602] ; National Natural Science Foundation of China[62176014] ; National Natural Science Foundation of China[62276015] ; National Natural Science Foundation of China[62206266] ; Fundamental Research Funds for the Central Universities ; Sichuan Science and Technology Program[2023NSFSC0032]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001336378400121
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41161
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Zhao; Zhuang, Fuzhen
作者单位	1.Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China 4.Zhongguancun Lab, Beijing 100191, Peoples R China 5.Southwestern Univ Finance & Econ, Inst Digital Econ & Interdisciplinary Sci Innovat, Fintech Innovat Ctr, Financial Intelligence & Financial Engn Key Lab Si, Chengdu 610074, Peoples R China 6.Beihang Univ, Sch Comp Sci, Beijing 100191, Peoples R China 7.Beijing Acad Blockchain & Edge Comp, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Fuwei,Zhang, Zhao,Zhuang, Fuzhen,et al. Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(11):7115-7128.
APA	Zhang, Fuwei,Zhang, Zhao,Zhuang, Fuzhen,Zhao, Yu,Wang, Deqing,&Zheng, Hongwei.(2024).Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(11),7115-7128.
MLA	Zhang, Fuwei,et al."Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.11(2024):7115-7128.