A Survey on Temporal Knowledge Graph Representation Learning Methods

doi:10.11871/jfdc.issn.2096-742X.2026.05.013

Abstract

Abstract:

[Purpose] Temporal knowledge graphs incorporate temporal information to model the dynamic evolution of entity relations, serving as a foundation for event prediction and dynamic relational modeling. This paper presents a systematic review of representation learning methods, analyzing their modeling paradigms and research trends, and providing a reference for research in related fields. [Methods] Existing approaches are categorized into four paradigms: time-enhanced embedding, tensor decomposition, graph neural networks, and history-based dynamic evolution. A unified comparison is conducted from three aspects: temporal modeling, structural dependency modeling, and historical information utilization. Commonly used datasets and evaluation metrics are also summarized. [Conclusions] Significant differences exist among methods in temporal granularity, structural modeling capability, and the exploitation of historical dependencies. Current approaches remain limited in modeling complex temporal patterns and long-term dependencies. Future work should emphasize multi-source information fusion and unified modeling frameworks.

Key words: temporal knowledge graph, representation learning, temporal modeling, dynamic evolution

SHEN Shuyi, WANG Yifan, BU Fanliang. A Survey on Temporal Knowledge Graph Representation Learning Methods[J]. Frontiers of Data and Computing, 2026, 8(5): 165-176, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.05.013.

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References 53

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基于历史事件序列的动态演化建模框架
输入：	历史事件序列
输出：	未来事件预测或事实补全结果
1：	初始化实体、关系和时间嵌入
2：	根据时间戳构建按时间排序的事件序列
3：	对每个时间步编码事件并更新隐藏状态
4：	计算下一事件概率
5：	输出预测结果

方法	代表模型	时间建模能力	结构依赖建模	历史信息利用	计算复杂度	适用场景	局限
时间增强嵌入方法	TTransE^[5]、 HyTE^[9]、ATiSE^[16]	离散时间建模，显式时间向量	基于三元组，较弱	无显式历史建模	低	大规模知识图谱补全	表达能力有限，难建模复杂关系和长期依赖
张量分解方法	T-SimplE^[23]、 T-ComplEx^[24]、TuckERTNT^[25]	时间与关系联合建模	隐式多关系建模，适中	以静态建模为主	较高	复杂关系模式建模	计算复杂度高，对稀疏数据较敏感
图神经网络方法	RE-GCN^[28]、 TiRGN^[30]、MGESL^[31]、RPHF-GNN^[34]	离散或连续时间建模（结合RNN、时间编码）	显式图结构建模，较强	部分历史融合	高	能利用图结构信息，表达能力强	计算开销大，大规模动态图训练困难
基于历史序列的动态演化方法	Know-Evolve^[36]、 RE-NET^[37]、 CyGNet^[39]、CEN^[40]、CognTKE^[44]	连续时间，事件驱动建模	部分结合图结构，适中	显式历史依赖建模	较高	事件预测与时间推理	历史序列长时计算开销大，易信息衰减

数据集	实体数	关系数	训练集	验证集	测试集	时间戳	时间间隔
ICEWS14	7,128	230	72,826	8,941	8,963	365	24hours
ICEWS05-15	10,488	251	386,962	46,275	46,092	4,017	24hours
ICEWS18	23,033	256	373,018	45,995	49,545	365	24hours
GDELT	7,691	240	1,734,399	238,765	305,241	2,975	15mins
YAGO	10,623	10	1,615,40	19,523	20,026	189	1year
WIKI	12,554	24	539,286	67,538	63,110	232	1year

Model	ICEWS14				ICEWS05-15				ICEWS18
Model	MRR	Hits@1	Hits@3	Hits@10	MRR	Hits@1	Hits@3	Hits@10	MRR	Hits@1	Hits@3	Hits@10
TTransE^[5]	25.5	7.4	-	60.1	27.1	8.4	-	61.6	-	-	-	-
TA-TransE^[10]	27.5	9.5	-	62.5	29.9	9.6	-	66.8	-	-	-	-
HyTE^[9]	29.7	10.8	41.6	65.5	31.6	11.6	44.5	68.1	-	-	-	-
ATiSE^[16]	55.0	43.6	62.9	75.0	51.9	37.8	60.6	79.4	-	-	-	-
DistMult^[20]	43.9	32.3	-	67.2	45.6	33.7	-	69.1	-	-	-	-
ComplEx^[21]	46.7	34.7	52.7	71.6	48.1	36.2	53.5	72.9	-	-	-	-
T-ComplEx^[24]	61.9	54.2	66.1	76.7	66.5	58.3	71.6	81.1	-	-	-	-
RE-GCN^[28]	40.4	30.7	45.0	59.2	48.0	37.3	53.9	68.3	30.6	21.0	34.3	48.8
CENET^[29]	39.0	29.6	43.2	57.5	42.0	32.2	47.0	60.4	27.9	18.2	31.6	47.0
TiRGN^[30]	44.0	33.8	49.0	63.8	50.0	39.3	56.1	70.7	33.7	23.2	37.1	54.2
RPHF-GNN^[34]	42.7	31.8	45.0	63.8	46.9	35.3	53.4	68.6	31.3	20.6	35.6	52.2
RE-NET^[37]	38.3	28.7	41.4	54.5	43.3	33.4	47.8	63.1	28.8	19.1	32.4	47.5
CyGNet^[39]	35.0	25.7	39.0	53.6	36.8	26.6	41.6	56.2	24.9	15.9	28.3	42.6
CognTKE^[44]	46.1	36.5	51.1	64.5	53.1	42.6	59.4	72.7	35.2	25.2	39.9	54.7