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arxiv: 2605.24064 · v1 · pith:NMKWGJKN · submitted 2026-05-22 · cs.LG · cs.AI

Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion

Jaejun Lee , Seheon Kim , Joyce Jiyoung Whang This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-30 15:31 UTCgrok-4.3pith:NMKWGJKNrecord.jsonopen to challenge →

classification cs.LG cs.AI
keywords hyper-relational knowledge graphsmasked discrete diffusionfact generationlink predictiongenerative representation learningknowledge graph completionKREPE
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The pith

KREPE uses masked discrete diffusion to generate valid hyper-relational facts from arbitrarily masked queries.

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

The paper introduces fact generation as a task that completes or creates hyper-relational facts when any number of their components may be missing at once. It proposes KREPE, which trains a masked discrete diffusion model to estimate the distributions over those missing parts given the observed ones and the broader graph structure. The model handles dependencies inside a single fact through contextual message passing and correlations across facts through stochastic sampling of contexts. This single framework covers both classic link prediction and the new generation setting. The authors report state-of-the-art link-prediction numbers and stronger novel-fact generation than LLM baselines.

Core claim

KREPE is the first generative representation learning method for HKGs that learns to model the probability distributions of missing components conditioned on the local fact components and global structure of HKGs via a masked discrete diffusion. KREPE models both the intra-fact dependencies by contextual message passing and inter-fact correlations by aggregating stochastically sampled contexts. KREPE seamlessly unifies link prediction and fact generation within a single training framework, achieving state-of-the-art performance on standard HKG link prediction benchmarks and outperforming LLM-based baselines in generating novel and correct facts.

What carries the argument

Masked discrete diffusion process that learns distributions over missing components by conditioning on local observed parts via contextual message passing and on global structure via stochastic context sampling.

If this is right

  • Link prediction reduces to the special case of fact generation with a single masked component.
  • The same trained model produces complete facts when any subset of components is masked.
  • The approach reports state-of-the-art scores on existing HKG link-prediction benchmarks.
  • Generated facts are rated more novel and correct than those produced by LLM baselines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If diffusion succeeds on arbitrary masks, the same conditioning mechanism could be tried on other multi-component structured objects such as molecular graphs or event records.
  • Treating variable numbers of blanks inside one training objective may reduce the need for separate models for each prediction setting.
  • The stochastic context sampling step suggests a route for incorporating long-range graph information without explicit message passing over the entire graph at every step.

Load-bearing premise

That a masked discrete diffusion process can reliably capture both intra-fact dependencies via contextual message passing and inter-fact correlations via stochastic context sampling for arbitrary masking patterns across multiple or all components of a hyper-relational fact.

What would settle it

Test KREPE on a held-out set of hyper-relational facts in which every component is masked and measure the fraction of generated facts that are valid according to the ground-truth graph; if the fraction does not exceed strong baselines, the modeling claim does not hold.

Figures

Figures reproduced from arXiv: 2605.24064 by Jaejun Lee, Joyce Jiyoung Whang, Seheon Kim.

Figure 1
Figure 1. Figure 1: Comparison of link prediction and fact generation tasks on HKGs. Link prediction retrieves a single missing element by ranking candidates, whereas fact generation constructs valid facts from arbitrarily masked inputs, including fully masked ones. bases such as Wikidata (Vrandeciˇ c & Kr ´ otzsch ¨ , 2014) and YAGO (Suchanek et al., 2007) extend the triplets into hyper￾relational facts by adding auxiliary k… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of KREPE. During training, the training set is partitioned into an observed set and a target set, where the facts in the target set are masked to serve as queries. Representations of the entities, relations, and masks are updated via contextual message passing, integrating both the local contexts within the facts and the relationship between facts. These representations are used to approximate the… view at source ↗
Figure 3
Figure 3. Figure 3: The prompt used to evaluate the correctness of generated facts. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The prompt used for fact generation across all settings in the Re-ranking baseline. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The prompt used for the Neighbor Sets baseline in the Scratch setting. Neighbor Sets Prompt Template - Targeted Role: You are an Expert Hyper-relational Fact Generator. Available entities: {entities list} Available relations: {relations list} Task: Generate {num candidates} DIFFERENT hyper-relational facts by completing the following MASKED fact. Each fact should be unique. Masked Fact: {masked fact} Const… view at source ↗
Figure 6
Figure 6. Figure 6: The prompt used for the Neighbor Sets baseline in the Targeted setting. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The prompt used for the Neighbor Sets baseline in the Arbitrary Masking setting Few-shot Facts Prompt Template - Scratch Role: You are an Expert Hyper-relational Fact Generator. Here are {num facts} existing facts for context: {facts} Task: Generate a NEW hyper-relational fact with length {fact length} Constraint: Each fact must contain exactly {fact length} elements. Only use use entities and relations th… view at source ↗
Figure 8
Figure 8. Figure 8: The prompt used for the Few-shot Facts baseline in the Scratch setting. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The prompt used for the Few-shot Facts baseline in the Targeted setting. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The prompt used for the Few-shot Facts baseline in the Arbitrary Masking setting. Random Facts Prompt Template - Scratch Role: You are an Expert Hyper-relational Fact Generator. Here are {num facts} hyper-relational facts for context: {facts} Task: Generate {generate size} NEW facts with target length {fact length} based on this list. Use entities, relations, and qualifier key-values only from the provide… view at source ↗
Figure 11
Figure 11. Figure 11: The prompt used for the Random Facts baseline in the Scratch setting. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The prompt used for the Random Facts baseline in the Targeted settings. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The prompt used for the Random Facts baseline in the Arbitrary Masking settings. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The prompt used for the Autoregressive baseline in the Scratch setting. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The prompt used for the Autoregressive baseline in the Targeted setting. Autoregressive Prompt Template - Arbitrary Masking User: Role: You are an Expert Hyper-relational Fact Generator. Current fact being built: {current fact} Fixed positions (CANNOT change): {anchor information} Here are facts for context: {context fact} Task: Select ONE {element type} from the facts above that could fill the [FILLING N… view at source ↗
Figure 16
Figure 16. Figure 16: The prompt used for the Autoregressive baseline in the Arbitrary Masking setting. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
read the original abstract

Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current methods cast it as a simple link prediction, assuming that nearly all entities and relations within a fact are known, leaving only a single blank to be filled. However, this restricted assumption may not hold in real-world scenarios in which multiple, or even all, constituent components of a fact may be missing simultaneously. To bridge this gap, we introduce a task called fact generation: generating a valid hyper-relational fact from an arbitrarily masked query, i.e., completing a partially observed fact or generating a fact from scratch. We propose KREPE, the first generative representation learning method for HKGs that learns to model the probability distributions of missing components conditioned on the local fact components and global structure of HKGs via a masked discrete diffusion. KREPE models both the intra-fact dependencies by contextual message passing and inter-fact correlations by aggregating stochastically sampled contexts. KREPE seamlessly unifies link prediction and fact generation within a single training framework, achieving state-of-the-art performance on standard HKG link prediction benchmarks and outperforming LLM-based baselines in generating novel and correct facts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes KREPE, the first generative representation learning method for hyper-relational knowledge graphs (HKGs) based on masked discrete diffusion. It learns to model the joint probability distributions over missing components of a hyper-relational fact conditioned on the observed local components and global graph structure. The approach uses contextual message passing to capture intra-fact dependencies and stochastic context sampling to capture inter-fact correlations, enabling a unified framework for both standard link prediction (single-component masks) and a new fact-generation task (arbitrary multi-component or full masks). The abstract asserts state-of-the-art results on HKG link-prediction benchmarks and superiority over LLM baselines on novel-fact generation.

Significance. If the central modeling claim and empirical results hold, the work would meaningfully extend HKG completion beyond the restrictive single-blank assumption of prior link-prediction methods toward more realistic arbitrary-missing-component scenarios. A single diffusion-based generative model that unifies the two tasks could reduce the need for task-specific architectures and provide a principled way to generate complete facts rather than merely ranking candidates.

major comments (2)
  1. [Abstract] Abstract: the central claim that KREPE 'achieves state-of-the-art performance on standard HKG link prediction benchmarks and outperforming LLM-based baselines in generating novel and correct facts' is asserted without any reported metrics, dataset sizes, ablation studies, baseline implementations, or result tables. This absence is load-bearing because the soundness of the method cannot be evaluated from the provided text.
  2. [Abstract] Abstract: the modeling claim that masked discrete diffusion, via contextual message passing and stochastic context sampling, reliably captures both intra-fact and inter-fact dependencies for arbitrary masking patterns (including cases where multiple or all components are masked) is presented at a high level without forward/reverse schedule details, message-passing architecture, or any empirical verification that the reverse process generalizes beyond single-component masks. This assumption is required for the unification of link prediction and fact generation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. The comments focus on the abstract's presentation of claims; we address each point below and indicate planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that KREPE 'achieves state-of-the-art performance on standard HKG link prediction benchmarks and outperforming LLM-based baselines in generating novel and correct facts' is asserted without any reported metrics, dataset sizes, ablation studies, baseline implementations, or result tables. This absence is load-bearing because the soundness of the method cannot be evaluated from the provided text.

    Authors: The abstract serves as a concise summary; all quantitative support—including metrics on WikiPeople and JF17K, dataset statistics, ablation studies, baseline details (e.g., specific LLM implementations), and result tables—appears in Sections 4 and 5 of the full manuscript. We agree the abstract could better anchor its claims and will revise it to include 1-2 key performance figures and dataset sizes within length constraints. revision: yes

  2. Referee: [Abstract] Abstract: the modeling claim that masked discrete diffusion, via contextual message passing and stochastic context sampling, reliably captures both intra-fact and inter-fact dependencies for arbitrary masking patterns (including cases where multiple or all components are masked) is presented at a high level without forward/reverse schedule details, message-passing architecture, or any empirical verification that the reverse process generalizes beyond single-component masks. This assumption is required for the unification of link prediction and fact generation.

    Authors: Forward/reverse schedules, the contextual message-passing architecture, and stochastic sampling are detailed in Sections 3.2–3.3. Empirical verification for arbitrary (including multi-component and full) masks, plus unification of the two tasks, is provided via experiments in Section 4.3 and Tables 2–3. The abstract intentionally remains high-level; we will not expand it with schedule specifics but can add a brief clause referencing the empirical support for multi-mask generalization if space allows. revision: partial

Circularity Check

0 steps flagged

No circularity; novel modeling proposal with no derivations or self-referential reductions

full rationale

The paper proposes KREPE, a new generative method based on masked discrete diffusion for HKGs, unifying link prediction and fact generation. The abstract describes the approach as modeling intra-fact dependencies via contextual message passing and inter-fact correlations via stochastic context sampling, but presents no equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations. No steps reduce by construction to inputs. This is a standard new-architecture paper; the central claim is the modeling choice itself, not a derived quantity. Matches reader's assessment of minimal circularity risk.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; all such details are absent from the provided text.

pith-pipeline@v0.9.1-grok · 5750 in / 1061 out tokens · 35420 ms · 2026-06-30T15:31:31.094615+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages · 1 internal anchor

  1. [1]

    BERT: Pre-training of deep bidirectional transformers for lan- guage understanding

    Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. BERT: Pre-training of deep bidirectional transformers for lan- guage understanding. InProceedings of the 2019 Confer- ence of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Short Papers), pp. 4171–4186,

    work page 2019

  2. [2]

    and Chen, L

    Di, S. and Chen, L. Message function search for knowl- edge graph embedding. InProceedings of the ACM Web Conference 2023, pp. 2633–2644,

    work page 2023

  3. [3]

    Searching to sparsify tensor decomposition for N-ary relational data

    Di, S., Yao, Q., and Chen, L. Searching to sparsify tensor decomposition for N-ary relational data. InProceedings of the Web Conference 2021, pp. 4043–4054,

    work page 2021

  4. [4]

    Message passing for hyper-relational knowl- edge graphs

    Galkin, M., Trivedi, P., Maheshwari, G., Usbeck, R., and Lehmann, J. Message passing for hyper-relational knowl- edge graphs. InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 7346–7359,

    work page 2020

  5. [5]

    Link prediction on N-ary relational data

    Guan, S., Jin, X., Wang, Y ., and Cheng, X. Link prediction on N-ary relational data. InProceedings of The World Wide Web Conference 2019, pp. 583–593,

    work page 2019

  6. [6]

    Gaussian Error Linear Units (GELUs)

    Hendrycks, D. and Gimpel, K. Gaussian error linear units (GELUs).arXiv preprint arXiv:1606.08415,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    LLM-based multi-level knowledge generation for few-shot knowl- edge graph completion

    Li, Q., Chen, Z., Ji, C., Jiang, S., and Li, J. LLM-based multi-level knowledge generation for few-shot knowl- edge graph completion. InProceedings of the 33rd Inter- national Joint Conference on Artificial Intelligence, pp. 2135–2143, 2024a. Li, Z., Wang, C., Wang, X., Chen, Z., and Li, J. HJE: Joint convolutional representation learning for knowledge hy...

    work page 2025

  8. [8]

    Role-aware modeling for N-ary relational knowledge bases

    Liu, Y ., Yao, Q., and Li, Y . Role-aware modeling for N-ary relational knowledge bases. InProceedings of the Web Conference 2021, pp. 2660–2671,

    work page 2021

  9. [9]

    Enhancing large language model for knowledge graph completion via structure-aware alignment-tuning

    Liu, Y ., Cao, Y ., Lin, X., Shang, Y ., Wang, S., and Pan, S. Enhancing large language model for knowledge graph completion via structure-aware alignment-tuning. InPro- ceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 20970–20984, 2025a. Liu, Y ., Tian, X., Sun, Z., and Hu, W. Finetuning generative large language m...

    work page 2025

  10. [10]

    HDiff: Confidence-guided denoising diffusion for robust hyper- relational link prediction

    Luo, X., Zheng, R., Huang, J., and Yu, H. HDiff: Confidence-guided denoising diffusion for robust hyper- relational link prediction. InFindings of the Association for Computational Linguistics: EMNLP 2025, pp. 7417– 7434,

    work page 2025

  11. [11]

    Beyond triplets: Hyper-relational knowledge graph embedding for link prediction

    Rosso, P., Yang, D., and Cudr´e-Mauroux, P. Beyond triplets: Hyper-relational knowledge graph embedding for link prediction. InProceedings of The Web Conference 2020, pp. 1885–1896,

    work page 2020

  12. [12]

    HyConvE: A novel embedding model for knowledge hypergraph link prediction with convolutional neural networks

    Wang, C., Wang, X., Li, Z., Chen, Z., and Li, J. HyConvE: A novel embedding model for knowledge hypergraph link prediction with convolutional neural networks. In Proceedings of the ACM Web Conference 2023, pp. 188– 198,

    work page 2023

  13. [13]

    Link prediction on N-ary relational facts: A graph-based approach

    Wang, Q., Wang, H., Lyu, Y ., and Zhu, Y . Link prediction on N-ary relational facts: A graph-based approach. InFind- ings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 396–407,

    work page 2021

  14. [14]

    KICGPT: Large language model with knowledge in context for knowledge graph completion

    Wei, Y ., Huang, Q., Zhang, Y ., and Kwok, J. KICGPT: Large language model with knowledge in context for knowledge graph completion. InFindings of the Associ- ation for Computational Linguistics: EMNLP 2023, pp. 8667–8683,

    work page 2023

  15. [15]

    Multi-perspective improve- ment of knowledge graph completion with large language models

    Xu, D., Zhang, Z., Lin, Z., Wu, X., Zhu, Z., Xu, T., Zhao, X., Zheng, Y ., and Chen, E. Multi-perspective improve- ment of knowledge graph completion with large language models. InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Re- sources and Evaluation, pp. 11956–11968,

    work page 2024

  16. [16]

    Scalable instance re- construction in knowledge bases via relatedness affiliated embedding

    Zhang, R., Li, J., Mei, J., and Mao, Y . Scalable instance re- construction in knowledge bases via relatedness affiliated embedding. InProceedings of the 2018 World Wide Web Conference, pp. 1185–1194,

    work page 2018

  17. [17]

    Additionally, we set zENT =x ENT and zREL =x REL for WikiPeople and WikiPeople−

    Bias terms are included in all linear projections, and dropout is employed. Additionally, we set zENT =x ENT and zREL =x REL for WikiPeople and WikiPeople−. In terms of regularization, we exclude bias terms and LayerNorm parameters from weight decay following common practice. For WikiPeople and WikiPeople−, weight decay is explicitly applied to the LayerN...

    work page 2019

  18. [18]

    Htrain, Hvalid, and Htest denote the training, validation, and test set, respectively. On WD50K and WikiPeople−, we select optimal hyperparameters using the validation set and report final results after retraining KREPE on the combined training and validation sets, by following the common practice for these datasets (Galkin et al., 2020; Wang et al., 2021...

    work page 2020

  19. [19]

    The peak learning rate and the minimum learning rate is denoted by ηmax and ηmin, respectively

    over a total of 2,000 epochs, incorporating a linear warmup phase for the first 200 epochs. The peak learning rate and the minimum learning rate is denoted by ηmax and ηmin, respectively. Validation is performed every 50 epochs to identify the best epoch. Additionally, we use a weight decay coefficient of 0.01 with AdamW for WD50K and 0.1 for WikiPeople a...

    work page 2025

  20. [20]

    Yes”. Otherwise, respond “No

    Table 13 shows that reducing steps decreases both runtime and accuracy. Yet, even with just 50 steps, the accuracy of KREPE (0.671) is nearly 2x higher than the best baseline, Gibbs Sampling (0.369), while being over 5× faster (0.53 sec vs. 2.81 sec). We note that KREPE supports efficient batch processing; for instance, generating a batch of 1,000 queries...

    work page 2020