Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities

Ze Zhao; Yuhui He; Lyuwen Wu; Gu Tang; Bin Lu; Xiaoying Gan; Luoyi Fu; Xinbing Wang

cs.AIApr 11, 2026

Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities

Authors: Ze Zhao, Yuhui He, Lyuwen Wu, Gu Tang, Bin Lu, Xiaoying Gan, Luoyi Fu, Xinbing Wang, +1 more

Organizations: Shanghai Jiao Tong University · IGSNRR, Chinese Academy of Sciences

Abstract

Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at capturing relational dynamics, their performance is limited by a closed-world assumption, which fails to account for emerging entities not present in the training. Notably, these entities continuously join the network without historical interactions. Empirical study reveals that emerging entities are widespread in TKGs, comprising roughly 25% of all entities. The absence of historical interactions of these entities leads to significant performance degradation in reasoning tasks. Whereas, we observe that entities with semantic similarities often exhibit comparable interaction histories, suggesting the presence of transferable temporal patterns. Inspired by this insight, we propose TransFIR (Transferable Inductive Reasoning), a novel framework that leverages historical interaction sequences from semantically similar known entities to support inductive reasoning. Specifically, we propose a codebook-based classifier that categorizes emerging entities into latent semantic clusters, allowing them to adopt reasoning patterns from similar entities. Experimental results demonstrate that TransFIR outperforms all baselines in reasoning on emerging entities, achieving an average improvement of 28.6% in Mean Reciprocal Rank (MRR) across multiple datasets. The implementations are available at https://github.com/zhaodazhuang2333/TransFIR.

Figures & tables

Figure 1: Illustration of Transductive vs. Inductive Reasoning on Emerging Entities.

Figure 2: (a) Entity emergence over time. Across four TKGs, new entities continuously emerge; about $\approx\!25\%$ of entities are unseen during training. (b) Performance comparison. Under Vanilla vs. Emerging settings, strong baselines consistently drop on emerging entity triples. (c) Representation collapse. On ICEWS14, t - SNE of LogCL shows representation collapsing after training, while known entities drift to a separate manifold. (d) Transferable patterns. Semantically similar entities share relation-conditioned patterns, enabling transfer to emerging entities.

Figure 3: The overall architecture of proposed TransFIR .

		ICEWS14			ICEWS18			ICEWS05-15			GDELT
Method		MRR	Hits@3	Hits@10	MRR	Hits@3	Hits@10	MRR	Hits@3	Hits@10	MRR	Hits@3	Hits@10
	CyGNet(2021)	0.0111	0.0098	0.0202	0.0031	0.0020	0.0047	0.0031	0.0020	0.0048	0.0067	0.0031	0.0147
	REGCN(2021)	0.1175	0.1263	0.2232	0.0947	0.1004	0.1797	0.0887	0.0961	0.1803	0.0222	0.0209	0.0393
	HiSMatch(2022)	0.0284	0.0285	0.0418	0.0055	0.0058	0.0076	0.0242	0.0238	0.0443	0.0159	0.0141	0.0270
	MGESL(2024)	0.0309	0.0361	0.0603	0.0747	0.0809	0.1031	0.1069	0.1166	0.1563	0.0516	0.0471	0.0815
	LogCL(2024)	0.1354	0.1770	0.2273	0.0903	0.1064	0.1548	0.1917	0.2452	0.2855	0.0473	0.0479	0.0973

Table 1: Performance comparison of inductive reasoning on emerging entities on four benchmarks. In each column, best results are highlighted in bold and second-best are underlined . For generative model GenTKG, MRR is unavailable due to it’s reliance on multiple generations for each query.

Figure 4: (a) t-SNE visualization showing the improved separation of clusters in TransFIR compared to LogCL, with Collapse Ratio improvement from 0.0055 to 0.8677. (b) Case analysis of three representative clusters and how TransFIR transfers reasoning patterns to emerging entities.

Figure 5: Ablation study results on four benchmarks, showing the performance impact of removing different components. Reported results are for Hits@10, with additional metrics in Appendix F.2 .

Figure 6: Experiment results on ICEWS14 under the Unknown and Emerging settings.

PLM	ICEWS14	ICEWS18	GDELT
Baseline	0.2273	0.1797	0.1131
T5	0.3057	0.2061	0.2082
RoBERTa	0.2934	0.1939	0.2289
Qwen3	0.2567	0.2009	0.2030
BERT	0.3246	0.2324	0.2278

Table 2: Different Textual Encoder experiment result on TransFIR .

Figure 7: GPU memory usage and empirical running time on ICEWS14. TransFIR achieves significantly lower peak GPU memory usage while maintaining competitive training speed.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Entities	Relation	Time Snapshots	Total Triples	Emerging Entities
ICEWS14	7128	230	365	90730	1301
ICEWS18	23033	256	304	468558	3434
ICEWS05-15	10488	251	4017	461329	1954
GDELT	7691	240	2976	2278405	1020

Appendix

Table 3: Statistics of all datasets, including ICEWS14, ICEWS18, ICEWS05-15 and GDELT.

Figure 8: t-SNE visualizations comparing LogCL and TransFIR on multiple datasets. The top row shows LogCL embeddings, with a clear representation collapse for emerging entities (red). The bottom row shows TransFIR , where emerging entities are well-separated into latent semantic clusters, significantly improving the Collapse Ratio across all datasets.

Figure 9: Ablation results on four benchmarks under MRR (top row) and Hits@3 (bottom row). The ranking of variants mirrors the main-paper Hits@10.

Figure 10: Results on ICEWS18, ICEWS05-15, and GDELT under the Unknown (blue; observable $G_{<t}$ ) vs. Emerging (green; zero history) settings. All methods improve with pre-query history, and TransFIR remains best on both MRR and Hits@10 across datasets.

Figure 11: Experiment results on ICEWS14 and ICEWS05-15 under different time splits.

Figure 12: Hyperparameter study on four benchmarks, exploring effects of codebook size

K

, Interaction Chain length

k

, hidden dimension

d

, and the number of layers

l

. Brown and green represent MRR and HITS@10, respectively.

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CID-TKG: Collaborative Historical Invariance and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning

Shuai-Long Lei, Xiaobin Zhu, Jiarui Liang, Guoxi Sun+2

University of Science and Technology Beijing, Haidian, Beijing, China

Temporal KGs Temporal Link Prediction

Open paper AskPDF arXiv

Temporal knowledge graph (TKG) reasoning aims to infer future facts at unseen timestamps from temporally evolving entities and relations. Despite recent progress, existing approaches still suffer from inherent limitations due to their inductive biases, as they predominantly rely on time-invariant or weakly time-dependent structures and overlook the evolutionary dynamics. To overcome this limitation, we propose a novel collaborative learning framework for TKGR (dubbed CID-TKG) that integrates evolutionary dynamics and historical invariance semantics as an effective inductive bias for reasoning. Specifically, CID-TKG constructs a historical invariance graph to capture long-term structural regularities and an evolutionary dynamics graph to model short-term temporal transitions. Dedicated encoders are then employed to learn representations from each structure. To alleviate semantic discrepancies across the two structures, we decompose relations into view-specific representations and align view-specific query representations via a contrastive objective, which promotes cross-view consistency while suppressing view-specific noise. Extensive experiments verify that our CID-TKG achieves state-of-the-art performance under extrapolation settings.

Abstract

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