Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs

Jiafeng Guo; Long Bai; Miao Su; Xiaolong Jin; Xueqi Cheng; Zhongni Hou; Zixuan Li

REVIEW 3 major objections 6 minor 46 references

Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read MT-Path is the first reinforcement-learning reasoner for n-tuple temporal knowledge graphs, predicting future facts by walking historical n-tuples and returning readable evidence paths; it reports higher MRR, Hits@1, and Hits@3 than all…

desk verdict A genuinely novel RL path model for N-tuple TKGs whose empirical SOTA claim is plausible but rests on unverifiable baseline adaptations and single-run results. read the letter →

arxiv 2505.12788 v1 pith:TQ34Z324 submitted 2025-05-19 cs.AI

Zhongni Hou , Miao Su , Xiaolong Jin , Zixuan Li , Long Bai , Jiafeng Guo , Xueqi Cheng This is my paper · ORCID

classification cs.AI

keywords temporalknowledgegraphsn-tuplereinforcementlearningmulti-hopreasoningexplainableAImixturepolicygraphneuralnetworkslinkprediction

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

MT-Path is a reinforcement-learning agent that answers queries about n-tuple temporal knowledge graphs — facts that carry extra role-entity pairs (such as "replaces: George W. Bush" or "series ordinal: 44th") on top of subject, predicate, object, and timestamp — by walking through historical facts step by step. The paper claims that splitting the next-hop decision into three low-level policies, one focused on the predicate alone, one on the core elements, and one on the whole fact, and combining them with a learned gate, lets the agent find evidence paths that are both more accurate and human-readable. If true, this would bring explainable multi-hop reasoning to a setting where previous N-TKG methods were black-box embedding models, and it would outperform all four families of baselines on the NICE and NWIKI benchmarks on mean reciprocal rank and Hits@1 and Hits@3.

What carries the argument

The load-bearing object is the mixture policy-driven action selector, which decomposes choosing the next hop into three low-level policies — the predicate-focused (P), core-element-focused (C), and whole-fact-focused (F) policies — and fuses their action scores with an MLP-based gate. A second component, the auxiliary element-aware GCN, enriches entity and predicate embeddings by message passing over recent facts, using augmented predicate embeddings built from the predicate plus auxiliary role-entity information. Together they let the agent weigh entity-irrelevant predicate information, core-element semantics, and full n-tuple detail, while the learned gate decides which view matters for the current query and history.

What would settle it

Reproduce the NICE and NWIKI experiments with a fully specified adaptation protocol for each TKG baseline, using the same time-aware filtered ranking and the same hyperparameter search budget; if MT-Path no longer beats the best adapted baseline on MRR, the paper's central performance claim fails.

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Extended reading notes

Core claim

The central claim is that MT-Path, a mixture-policy reinforcement-learning agent, is an effective and explainable method for N-TKG reasoning. It formulates reasoning as a Markov decision process over historical n-tuples, starting from the query's core entity and restricting actions to facts in which the current entity is itself a core element. At each step, three policies score candidate actions from different views: the predicate-focused policy uses only the predicate and the time interval, the core-element-focused policy adds the other core entity, and the whole-fact-focused policy adds all auxiliary role-entity pairs; an MLP gate weights the three scores. An auxiliary element-aware GCN passes messages over recent facts to give the agent embeddings that encode semantic dependencies between facts. Trained with REINFORCE and a time-shaped reward, the agent reports higher MRR, Hits@1, and Hits@3 than all baselines on NICE and NWIKI, and the paper shows example chains of historical facts supporting individual predictions.

Load-bearing premise

The headline comparison assumes that the quadruple-based TKG baselines were adapted to n-tuples without weakening them, but the paper never specifies the adaptation procedure or hyperparameters used.

Editorial extensions

If this is right

N-TKG predictions come with explicit, inspectable chains of historical n-tuples, so users can see which past facts the model relied on rather than receiving an embedding score.
The predicate-focused policy keeps reasoning viable for queries involving unseen entities, because the next-hop choice does not depend solely on entity embeddings the model has never seen.
Because the whole-fact-focused policy contributes the largest gain in the ablation, auxiliary role-entity pairs carry signal that quadruple-only TKG methods cannot exploit.
The strict search over core-element facts explains why Hits@10 can trail NE-Net and Cluster: MT-Path sacrifices recall for path quality and interpretability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural extension the paper leaves implicit is a hybrid that ranks MT-Path's paths with an embedding-based reranker; this could recover Hits@10 without abandoning the evidence paths.
The predicate-focused policy suggests a testable inductive setting: train on one set of entities and evaluate predictions where query entities are entirely new, measuring whether predicate-level patterns transfer.
The same three-view policy decomposition could apply to hyper-relational knowledge graphs without timestamps, where role-entity pairs play the role of auxiliary elements.
One could instrument the learned gate and test whether its weight on the predicate-focused policy rises for rare or unseen query entities, which would explain where the model's interpretability comes from.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First, what to know: MT-Path is the first RL-based multi-hop reasoning method for n-tuple TKGs, and the core design—three low-level policies over predicate, core elements, and whole facts plus an element-aware GCN—is a clean way to handle n-ary structure. The ablation study is actually informative: removing the F-Policy hurts most, and the P-Policy helps on unseen entities. That gives me some confidence the mixture matters.

Where I'd be cautious: the headline SOTA claim rests on comparing with TKG baselines that, by the paper's own admission in Section 2, 'cannot be directly applied to n-tuples and have to take adaptation measures.' Section 5.1 never says what those measures were. That is a real problem. The adaptation determines the action space and rule body for TLogic/TR-Rules and the graph search for TITer/Cluster/xERTE, so it is not a minor detail. On NICE the gain over NE-Net is small (MRR 49.91 vs 48.98), so a modest handicap in the adapted baselines could flip the comparison. I am not saying the adaptation was unfair—there is no evidence of that—but the paper does not let us check.

Also, all metrics are single-run point estimates. No standard deviations or significance tests. For a RL method with REINFORCE, that variance could be nontrivial. No code release either. These are standard requests, not fatal flaws.

The math and notation are coherent. The MDP formulation restricts actions to facts where the current entity is a core entity, the reward uses a time-shaped Dirichlet estimate, and the mixture gate is straightforward. I do not see a circularity problem. The limitations section is honest about not handling auxiliary entity prediction and scalability.

Bottom line: this is a useful, novel contribution to a niche but active area. The explainability claim is supported by case studies, not just a hand-wave. But the empirical comparison is not fully verifiable as reported. I would send it to peer review with a request for baseline adaptation details, multiple seeds, and code. A serious referee could get it to a good state.

Referee Report

3 major / 6 minor

Summary. The paper proposes MT-Path, a reinforcement learning (RL) based multi-hop reasoning method for n-tuple temporal knowledge graphs (N-TKGs). MT-Path models reasoning as a sequential decision process in which an agent traverses historical n-tuples using a mixture policy-driven action selector composed of three low-level policies: a predicate-focused policy, a core-element-focused policy, and a whole-fact-focused policy. A gating network adaptively combines the outputs of these policies, and an auxiliary element-aware GCN captures semantic dependencies among historical facts. The agent is trained with REINFORCE and a time-shaped reward. Experiments on NICE and NWIKI compare MT-Path with static n-tuple models, embedding-based TKG models, multi-hop TKG models, and the N-TKG model NE-Net, reporting improvements in MRR, Hits@1, and Hits@3. The paper also includes ablations, a seen/unseen entity analysis, and case studies illustrating the discovered reasoning paths.

Significance. If the empirical results are sound, the paper makes a useful contribution by being the first to apply RL to n-tuple temporal knowledge graph reasoning, providing an interpretable alternative to black-box embedding methods. The mixture policy design is a reasonable and well-motivated decomposition of the action selection problem, and the auxiliary element-aware GCN is a sensible mechanism for capturing inter-fact dependencies. The ablation study and case studies are valuable for understanding which components drive performance. However, the significance is conditional on the fairness of the baseline comparison and on the reliability of the reported metrics, both of which currently have important gaps.

major comments (3)

[§5.1 (Baselines)] The adaptation of the six multi-hop TKG baselines (TITer, Cluster, xERTE, TLogic, LCGE, and TR-Rules) to n-tuples is never described. Section 2 states that these methods 'cannot be directly applied to n-tuples and have to take adaptation measures', but Section 5.1 only lists them as baselines and does not specify the representation used for n-tuples, the modified action space or rule mining procedure, or the hyperparameters employed. Because the central claim is that MT-Path outperforms all baselines, the adaptation step is load-bearing: a treatment that flattens or discards auxiliary role-entity pairs could systematically handicap the baselines. Please provide the exact adaptation protocol, ideally with code or detailed pseudo-code, so that the comparison is reproducible and independently checkable.
[§5.2, Table 2] The claim that 'MT-Path outperforms all baselines on two datasets in terms of MRR, Hits@1, and Hits@3' is contradicted by the paper's own Table 2. On NWIKI, TLogic achieves Hits@3 of 81.95, while MT-Path achieves 81.90. This is a concrete factual error in the headline comparative claim. Please correct the claim or qualify it (e.g., state that MT-Path is best on MRR and Hits@1 on both datasets, and best on Hits@3 on NICE only; or report the actual comparison accurately).
[§5.2, Table 2 and §5.3, Table 3] All reported metrics are single-run point estimates without standard deviations, confidence intervals, or significance tests. The margins over the closest baseline are small in several cases, for example on NICE MRR (49.91 versus 48.98 for NE-Net) and Hits@1 (40.16 versus 38.36). Without variance estimates across multiple random seeds, the reader cannot judge whether the observed improvements are stable or within run-to-run noise. Please report the mean and standard deviation over at least three seeds and, where appropriate, a significance test such as a paired bootstrap or Wilcoxon test.

minor comments (6)

[§4.2, Eq. (4)] The index notation in Eq. (4) is inconsistent: the text uses both h_{t,k}^u and h^{k,t}_u, and the second term W^{k-1}_1 h^{k}_u appears to use the representation of layer k on the right-hand side while computing layer k+1. Please align the notation and verify that the self-loop term uses h^{k-1}_u.
[§5.3, Table 3] In Table 3, the rows for '-SC' and '-GA' report identical NWIKI values (74.48, 82.11, 78.52) across Hits@1, Hits@3, and MRR. This is surprising for two different ablations; please verify that these numbers are correct and not a copying error.
[§5.3] The text says 'compared with MA-Path' when referring to the full model; this should be 'MT-Path'.
[Figure 3] The bars in Figure 3 lack axis labels and a clear caption; please label the y-axis (e.g., MRR) and explain what the paired bars represent. Also clarify whether the reported values are averaged over multiple runs.
[§5.5] The last sentence of the case study says 'different lengthy' and should be 'different lengths'.
[Throughout] Baseline names are capitalized inconsistently (e.g., 'TITER' in Section 2 versus 'TITer' in Table 2, 'Cluster' versus 'cluster'). Please standardize the spelling and ensure that the reference list entries match the in-text citations.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claim is an empirical benchmark comparison on held-out test splits with no fitted parameter renamed as a prediction and no load-bearing self-citation chain.

full rationale

MT-Path's central claim is empirical: the model is trained on historical n-tuples and evaluated on held-out test facts from NICE and NWIKI, with MRR and Hits@k computed against standard filtered ranking protocols. The mixture policy, gate, and auxiliary element-aware GCN are model components whose contributions are tested by ablations, not quantities defined in terms of the target metric. The reward in Eq. (2) uses the true target entity to train the RL agent, but evaluation is on unseen queries, so this is standard supervised training rather than a fitted constant being called a prediction. The paper cites prior work by the same group for the NE-Net baseline and for the NICE/NWIKI datasets, but these are used as external comparison points and benchmarks, not as premises that define the reported outcome; no uniqueness theorem or prior result is invoked to forbid alternatives. The skeptical concern that quadruple-based TKG baselines required unspecified adaptation steps is a legitimate correctness and reproducibility risk, but it is not circularity: the paper's own equations do not reduce any claimed result to its inputs. No step in the derivation chain exhibits self-definition, fitted-input-as-prediction, or ansatz-smuggling via citation. The score is therefore 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on empirical assumptions about N-TKG structure (two core entities per fact), a modeling heuristic restricting actions to core-element facts, a recent-history window for the GCN, a Dirichlet-shaped reward, and the standard RL policy gradient. Several hyperparameters that affect the model (w, m, L) are chosen by hand and reported without sensitivity analysis. No new physical or conceptual entities are introduced, and the empirical evaluation is grounded in external test sets.

free parameters (4)

w (augmented predicate embedding weight)
In Equation 5, w in (0,1) blends the predicate embedding and auxiliary embedding for the GCN. No value or tuning range is reported in Section 5.1.
m (number of recent timestamps in SemComp) = 5 for NICE, 1 for NWIKI
Equation 3 builds the background graph from the last m snapshots. The values are chosen per dataset in Section 5.1 and no sensitivity analysis is given.
Maximum path length L = 3
Section 5.1 sets L=3 for both datasets. This caps the depth of the reasoning paths and directly bounds the search space.
Dirichlet parameters for p_delta_t_L
Equation 2 uses a time-shaped reward with p_delta_t_L, estimated from a Dirichlet distribution. The estimation procedure and prior parameters are not specified.

assumptions (5)

domain assumption Each fact in an N-TKG has exactly two core entities (e1, e2) and all other role-entity pairs are auxiliary; reasoning only predicts core entities.
Section 3 defines this structure, and the query form, action space, and reward in Section 4 depend on it. The limitations section acknowledges that auxiliary entity prediction is not addressed.
ad hoc to paper Only historical facts in which the current entity serves as a core element are valid actions.
Equation 1 restricts the action set to facts where the current entity is a core entity, justified as 'crucial' without empirical validation. No experiment tests whether including auxiliary-role facts would improve recall.
domain assumption The latest m timestamps contain sufficient semantic dependencies for prediction.
Equation 3 builds the background graph only over the window [t-m, t-1]. There is no analysis varying the window size, and the chosen m differs between the two datasets.
ad hoc to paper Answer entity occurrence probabilities can be modeled with a Dirichlet distribution.
Equation 2 multiplies the indicator reward by 1+p_delta_t_L, where p_delta_t_L is a Dirichlet-based estimate of the probability of the answer entity at the reached timestamp. This is an assumed distributional form, not a derived result.
standard math REINFORCE provides unbiased gradient estimates for the expected reward.
Section 4.4 optimizes the expected reward with REINFORCE. The policy gradient theorem is used as a background result without derivation.

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Cite this review

Pith. "Pith review of Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs." pith.science (2026). https://pith.science/paper/TQ34Z324

@misc{pith2026250512788,
  author       = {Pith},
  title        = {Pith review of: Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQ34Z324}},
  note         = {Machine review of arXiv:2505.12788}
}

read the original abstract

Temporal Knowledge Graphs (TKGs), which utilize quadruples in the form of (subject, predicate, object, timestamp) to describe temporal facts, have attracted extensive attention. N-tuple TKGs (N-TKGs) further extend traditional TKGs by utilizing n-tuples to incorporate auxiliary elements alongside core elements (i.e., subject, predicate, and object) of facts, so as to represent them in a more fine-grained manner. Reasoning over N-TKGs aims to predict potential future facts based on historical ones. However, existing N-TKG reasoning methods often lack explainability due to their black-box nature. Therefore, we introduce a new Reinforcement Learning-based method, named MT-Path, which leverages the temporal information to traverse historical n-tuples and construct a temporal reasoning path. Specifically, in order to integrate the information encapsulated within n-tuples, i.e., the entity-irrelevant information within the predicate, the information about core elements, and the complete information about the entire n-tuples, MT-Path utilizes a mixture policy-driven action selector, which bases on three low-level policies, namely, the predicate-focused policy, the core-element-focused policy and the whole-fact-focused policy. Further, MT-Path utilizes an auxiliary element-aware GCN to capture the rich semantic dependencies among facts, thereby enabling the agent to gain a deep understanding of each n-tuple. Experimental results demonstrate the effectiveness and the explainability of MT-Path.

Figures

Figures reproduced from arXiv: 2505.12788 by the authors.

**Figure 2.** An illustrative diagram of the proposed MT-Path model for multi-hop reasoning over N-TKGs. For the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Performance of MT-Path over queries involv [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Works this paper leans on

46 extracted references · 30 canonical work pages

[1]

Ling Chen, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, and Yongjun Zhang. 2021. Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(3):1--18

work page 2021
[2]

Songgaojun Deng, Huzefa Rangwala, and Yue Ning. 2020. Dynamic knowledge graph based multi-event forecasting. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1585--1595

work page 2020
[3]

Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, and Volker Tresp. 2023. Exploring link prediction over hyper-relational temporal knowledge graphs enhanced with time-invariant relational knowledge. arXiv preprint arXiv:2307.10219

arXiv 2023
[4]

Bahare Fatemi, Perouz Taslakian, David Vazquez, and David Poole. 2021. Knowledge hypergraphs: prediction beyond binary relations. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 2191--2197

work page 2021
[5]

Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann. 2020. Message passing for hyper-relational knowledge graphs. arXiv preprint arXiv:2009.10847

arXiv 2020
[6]

Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2020. Neuinfer: Knowledge inference on n-ary facts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6141--6151

work page 2020
[7]

Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2021. Link prediction on n-ary relational data based on relatedness evaluation. IEEE Transactions on Knowledge and Data Engineering, 35(1):672--685

work page 2021
[8]

Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, and Xueqi Cheng. 2019. Link prediction on n-ary relational data. In The World Wide Web Conference, pages 583--593

work page 2019

Show all 46 references

[9]

Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2020 a . Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In International Conference on Learning Representations

2020
[10]

Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2020 b . xerte: Explainable reasoning on temporal knowledge graphs for forecasting future links. arXiv preprint arXiv:2012.15537

2020 arXiv
[11]

Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8352--8364

2021
[12]

He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017. Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. arXiv preprint arXiv:1704.07130

2017 arXiv
[13]

Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Saiping Guan, Yutao Zeng, Jiafeng Guo, and Xueqi Cheng. 2023. Temporal knowledge graph reasoning based on n-tuple modeling. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1090--1100

2023
[14]

Zhiwei Hu, V \' ctor Guti \'e rrez-Basulto, Zhiliang Xiang, Ru Li, and Jeff Z Pan. 2023. Hyperformer: Enhancing entity and relation interaction for hyper-relational knowledge graph completion. In Proceedings of the 32nd ACM International Conference on Information and Knowledge...

2023
[15]

Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. In EMNLP

2020
[16]

Ningyuan Li, E Haihong, Shi Li, Mingzhi Sun, Tianyu Yao, Meina Song, Yong Wang, and Haoran Luo. 2023. Tr-rules: Rule-based model for link forecasting on temporal knowledge graph considering temporal redundancy. In Findings of the Association for Computational Linguistics: EMNL...

2023
[17]

Yujia Li, Shiliang Sun, and Jing Zhao. 2022 a . Tirgn: time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vi...

2022
[18]

Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, and Xueqi Cheng. 2022 b . Complex evolutional pattern learning for temporal knowledge graph reasoning. arXiv preprint arXiv:2203.07782

2022 arXiv
[19]

Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, and Xueqi Cheng. 2022 c . Hismatch: Historical structure matching based temporal knowledge graph reasoning. arXiv preprint arXiv:2210.09708

2022 arXiv
[20]

Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2021 a . Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs. arXiv preprint arXiv:2106.00327

2021 arXiv
[21]

Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. 2021 b . Temporal knowledge graph reasoning based on evolutional representation learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Deve...

2021
[22]

Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2023. Learn from relational correlations and periodic events for temporal knowledge graph reasoning. In Proceedings of the 46th international ACM SIGIR conference on research and ...

2023
[23]

Xinyue Liu, Jianan Zhang, Chi Ma, Wenxin Liang, Bo Xu, and Linlin Zong. 2024. Temporal knowledge graph reasoning with dynamic hypergraph embedding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-C...

2024
[24]

Yu Liu, Quanming Yao, and Yong Li. 2021. Role-aware modeling for n-ary relational knowledge bases. In Proceedings of the Web Conference 2021, pages 2660--2671

2021
[25]

Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, and Volker Tresp. 2022. Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 4120--4127

2022
[26]

Guanglin Niu and Bo Li. 2023. Logic and commonsense-guided temporal knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4569--4577

2023
[27]

Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong. 2022. Evokg: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs. In Proceedings of the Fifteenth ACM International Conference on Web Sear...

2022
[28]

Paolo Rosso, Dingqi Yang, and Philippe Cudr \'e -Mauroux. 2020. Beyond triplets: hyper-relational knowledge graph embedding for link prediction. In Proceedings of The Web Conference 2020, pages 1885--1896

2020
[29]

Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, and Hui Liu. 2023. Learning representations for hyper-relational knowledge graphs. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pages 253--257

2023
[30]

Alessio Signorini, Alberto Maria Segre, and Philip M Polgreen. 2011. The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic. PloS one, 6(5):e19467

2011
[31]

Haohai Sun, Shangyi Geng, Jialun Zhong, Han Hu, and Kun He. 2022. Graph hawkes transformer for extrapolated reasoning on temporal knowledge graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7481--7493

2022
[32]

Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, and Kun He. 2021. Timetraveler: Reinforcement learning for temporal knowledge graph forecasting. arXiv preprint arXiv:2109.04101

2021 arXiv
[33]

Xing Tang and Ling Chen. 2023. Gtrl: An entity group-aware temporal knowledge graph representation learning method. IEEE Transactions on Knowledge and Data Engineering

2023
[34]

Xing Tang, Ling Chen, Hongyu Shi, and Dandan Lyu. 2024. Dhyper: A recurrent dual hypergraph neural network for event prediction in temporal knowledge graphs. ACM Transactions on Information Systems, 42(5):1--23

2024
[35]

Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019. Composition-based multi-relational graph convolutional networks. In International Conference on Learning Representations

2019
[36]

Denny Vrande c i \'c and Markus Kr \"o tzsch. 2014. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78--85

2014
[37]

Quan Wang, Haifeng Wang, Yajuan Lyu, and Yong Zhu. 2021. Link prediction on n-ary relational facts: A graph-based approach. arXiv preprint arXiv:2105.08476

2021 arXiv
[38]

Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8:229--256

1992
[39]

Siheng Xiong, Yuan Yang, Faramarz Fekri, and James Clayton Kerce. 2024. Tilp: Differentiable learning of temporal logical rules on knowledge graphs. arXiv preprint arXiv:2402.12309

2024 arXiv
[40]

Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pages 1434--1443

2022
[41]

Donghan Yu and Yiming Yang. 2021. Improving hyper-relational knowledge graph completion. arXiv preprint arXiv:2104.08167

2021 arXiv
[42]

Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, and Liang Wang. 2023. Learning latent relations for temporal knowledge graph reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12617--12631

2023
[43]

Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, and Lei Zhao. 2023. Dream: Adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning. In Proceedings of the 46th International ACM SIGIR Conference on Research an...

2023
[44]

Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhan. 2020. Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. arXiv preprint arXiv:2012.08492

2020 arXiv
[45]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Ling Chen, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, and Yongjun Zhang. 2021. Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(3):1--18

work page 2021

[2] [2]

Songgaojun Deng, Huzefa Rangwala, and Yue Ning. 2020. Dynamic knowledge graph based multi-event forecasting. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1585--1595

work page 2020

[3] [3]

Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, and Volker Tresp. 2023. Exploring link prediction over hyper-relational temporal knowledge graphs enhanced with time-invariant relational knowledge. arXiv preprint arXiv:2307.10219

arXiv 2023

[4] [4]

Bahare Fatemi, Perouz Taslakian, David Vazquez, and David Poole. 2021. Knowledge hypergraphs: prediction beyond binary relations. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 2191--2197

work page 2021

[5] [5]

Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann. 2020. Message passing for hyper-relational knowledge graphs. arXiv preprint arXiv:2009.10847

arXiv 2020

[6] [6]

Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2020. Neuinfer: Knowledge inference on n-ary facts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6141--6151

work page 2020

[7] [7]

Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2021. Link prediction on n-ary relational data based on relatedness evaluation. IEEE Transactions on Knowledge and Data Engineering, 35(1):672--685

work page 2021

[8] [8]

Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, and Xueqi Cheng. 2019. Link prediction on n-ary relational data. In The World Wide Web Conference, pages 583--593

work page 2019