temporal knowledge graph representation learning with local and global evolutions

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Computer Science ›› 2022 , Vol. 49 ›› Issue (9) : 162-171. doi: 10.11896/jsjkx.220500204

• Artificial Intelligence • Previous Articles     Next Articles

Temporal Knowledge Graph Representation Learning

XU Yong-xin 1,2 , ZHAO Jun-feng 1,2,3 , WANG Ya-sha 1,2,3 , XIE Bing 1,2,3 , YANG Kai 1,2,3   

• 1 School of Computer Science,Peking University,Beijing 100871,China 2 Key Laboratory of High Confidence Software Technologies,Ministry of Education,Beijing 100871,China 3 Peking University Information Technology Institute(Tianjin Binhai),Tianjin 300450,China
• Received: 2021-10-22 Revised: 2022-05-16 Online: 2022-09-15 Published: 2022-09-09
• About author: XU Yong-xin,born in 1998,postgraduate.His main research interests include knowledge graph and so on. ZHAO Jun-feng,born in 1974,Ph.D,research professor,is a member of China Computer Federation.Her main research interests include big data analysis,knowledge graph,urban computing and so on.
• Supported by: National Natural Science Foundation of China(62172011).
• 1. 探讨2016版国际胰瘘研究小组定义和分级系统对胰腺术后患者胰瘘分级的影响.PDF (500KB)

Abstract: As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected.

Key words: Knowledge graph, Deep learning, Representation learning, Temporal information, Dynamic process

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XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning[J].Computer Science, 2022, 49(9): 162-171.

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Abstract: Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.

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Knowledge-Based Systems 2022  ·  Jiasheng Zhang , Shuang Liang , Yongpan Sheng , Jie Shao · Edit social preview

Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKG to low-dimensional vector space while preserving the evolutionary nature of TKG. Most existing methods separately treat knowledge that happened at different times, which fails to explore how temporal knowledge graph evolves over time. Actually, TKG should evolve both on local and global structures. The local structure evolution describes the formation process of graph structure in a detailed manner, while the global structure evolution refers to the dynamic topology (e.g., community partition) of graph, which is derived from the continuous formation process. Both of them are key factors for understanding the evolutionary nature of TKG. Unfortunately, seldom attention has been paid to this aspect. In this paper, we propose a new TKG representation learning framework with local and global structure evolutions, named EvoExplore. Specifically, we define the local structure evolution as the establishment process of relations between entities, and propose a hierarchical-attention-based temporal point process to capture the formation process of graph structure in a fine-grained manner. For global structure evolution, we propose a novel soft modularity parameterized by entity representations to capture the dynamic community partition of TKG. Finally, we employ a multi-task loss function to jointly optimize the above two parts, which allows EvoExplore to learn the mutual influences of local and global structure evolutions. Experimental results on three realworld datasets demonstrate the superiority of EvoExplore compared with baseline methods.

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Papers on Temporal Knowledge Graph Embedding and Reasoning

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Useful research resources

Graph-based Deep Learning Literature, Github

links to conference publications in graph-based deep learning

Reinforcement learning on graphs: A survey, Github

This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.

Awesome Machine Learning for Combinatorial Optimization Resources, Github

Awesome machine learning for combinatorial optimization papers.

Awesome-TKGC, Github

A collection of papers and resources about temporal knowledge graph completion (TKGC).

AKGR: Awesome Knowledge Graph Reasoning, Github

AKGR: Awesome Knowledge Graph Reasoning is a collection of knowledge graph reasoning works, including papers, codes and datasets.

Awesome Knowledge Graph, Github

A curated list of Knowledge Graph related learning materials, databases, tools and other resources.

Awesome-DynamicGraphLearning, Github

Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

KGE, Github

Some papers on Knowledge Graph Embedding(KGE)

KGLQ, Github

Some papers about Logical Query on Knowledge Graphs (KGLQ)

ADGC: Awesome Deep Graph Clustering, Github

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

Graph Adversarial Learning Literature, Github

A curated list of adversarial attacks and defenses papers on graph-structured data.

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Knowledge Graph Embedding: An Overview , APSIPA Transactions on Signal and Information Processing, 2024. paper Ge, X., Wang, Y. C., Wang, B., & Kuo, C. C. J

Survey of Temporal Knowledge Graph Completion Methods , Journal of Computer Engineering & Applications, 2024. paper Lei, X. I. A. O., & Qi, L. I.

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A Survey on Temporal Knowledge Graph: Representation Learning and Applications , ArXiv, 2024. paper Cai, Li, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, and Man Lan.

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects , ArXiv, 2023. paper

Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, Wen Gao

Knowledge Graphs: Opportunities and Challenges , Artificial Intelligence Review, 2023, paper

Ciyuan Peng, Feng Xia, Mehdi Naseriparsa & Francesco Osborne

Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs , ArXiv, 2023. paper

Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen

A Comprehensive Survey on Automatic Knowledge Graph Construction , ArXiv, 2023. paper

Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu

Temporal Knowledge Graph Completion: A Survey ArXiv, 2022. paper

Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li.

Update : Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li, Temporal Knowledge Graph Completion: A Survey , 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. Pages 6545-6553. paper

Reasoning over different types of knowledge graphs: Static, temporal and multi-modal , ArXiv, 2022. paper

A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks , Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, paper

Sulin Chen & Jingbin Wang

Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs . 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. paper

Survey on Temporal Knowledge Graph , 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC). paper

Chong Mo; Ye Wang; Yan Jia; Qing Liao

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Temporal Knowledge Graph Entity Alignment via Representation Learning

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• First Online: 08 April 2022
• Cite this conference paper

• Xiuting Song 16 ,
• Luyi Bai 16 ,
• Rongke Liu 16 &
• Han Zhang 16  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13246))

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• International Conference on Database Systems for Advanced Applications

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Entity alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing methods mainly focused on the static KG, which assumes that the relationship between entities is permanent. However, almost every KG will evolve over time in practical applications, resulting in the need for entity alignment between such temporal knowledge graphs (TKGs). In this paper, we propose a novel entity alignment framework suitable for TKGs, namely Tem-EA. To incorporate temporal information, we use recurrent neural networks to learn temporal sequence representations. Furthermore, we use graph convolutional network (GCN) and translation-based embedding model to fully learn structural information representation and attribute information representation. Based on these two representations, the entity similarity is calculated separately and combined using linear weighting. To improve the accuracy of entity alignment, we also propose a concept of nearest neighbor matching, which matches the most similar entity pair according to distance matrix. Experiments show that our proposed model has a significant improvement compared to previous methods.

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Acknowledgment

The work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2019501030), the Key Project of Scientific Research Funds in Colleges and Universities of Hebei Education Department (ZD2020402), the Fundamental Research Funds for the Central Universities (N2023019), and in part by the Program for 333 Talents in Hebei Province (A202001066).

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Xiuting Song, Luyi Bai, Rongke Liu & Han Zhang

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Song, X., Bai, L., Liu, R., Zhang, H. (2022). Temporal Knowledge Graph Entity Alignment via Representation Learning. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_30

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