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CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read ChainsFormer claims numerical reasoning over knowledge graphs improves when multi-hop inference is profiled as Relation-Attribute Chains, filtered in hyperbolic space and aggregated by attention, achieving up to 20.0% lower MAE than prior…

desk verdict Chain-based numerical reasoning idea is fresh, but the SOTA claim is undercut by omitting the two most relevant baselines and a few internal inconsistencies. read the letter →

arxiv 2504.14282 v1 pith:G446X6FN submitted 2025-04-19 cs.AI cs.LG

Ze Zhao , Bin Lu , Xiaoying Gan , Gu Tang , Luoyi Fu , Xinbing Wang This is my paper · ORCID
classification cs.AIcs.LG
keywords knowledgegraphreasoningnumericalattributepredictionrelation-attributechainsmulti-hophyperbolicembeddingchainofthoughttransformercompletion
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

The paper tries to establish that replacing homogeneous graph aggregation with explicit multi-hop chains makes numerical attribute prediction on knowledge graphs more accurate and more transparent. It introduces Relation-Attribute Chains (RA-Chains), which record only the sequence of attribute and relation types along a reasoning path, and builds a full pipeline that retrieves candidate chains, filters them with hyperbolic geometry, encodes them with a Transformer, and weights their predictions by attention. On two benchmark datasets the method reports mean absolute error reductions of 20.0% on YAGO15K and 7.4% on FB15K-237 over the strongest baselines. If true, this would mean that numerical reasoning is better understood as sequential chain reasoning than as neighbor aggregation.

What carries the argument

The central object is the Relation-Attribute Chain (RA-Chain), a tokenized logic rule body $(a_p, r_1, r_2, \ldots, r_l, a_q)$ that captures a multi-hop reasoning pattern using only attributes and relations. The argument is carried by three mechanisms: query-guided random walks assemble candidate RA-Chains into a Tree of Chains; a hyperbolic affinity score filters this tree by measuring hyperbolic distances both between the chain and the query attribute and between the known and query attributes; and an attention-based numerical reasoner weights each chain's scaled prediction. A Numerical-Aware Affine Transfer maps numerical magnitudes into the chain representation so that attributes with very different scales, such as population near $10^9$ and height near 1, can be handled together.

What would settle it

Construct a test set where two different entities share identical RA-Chains leading to the query attribute but have genuinely different target values; if ChainsFormer cannot separate these cases and a variant with entity identity included performs better, the central entity-free premise would fail.

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Extended reading notes

Core claim

The paper's central claim is that numerical reasoning on knowledge graphs can be cast as learning over logic chains rather than over graph neighborhoods. A Relation-Attribute Chain (RA-Chain) is an ordered sequence $(a_p, r_1, r_2, \ldots, r_l, a_q)$ containing only an initial attribute type, the relation types along the path, and the query attribute type, with entity identities deliberately removed. ChainsFormer retrieves these chains by random walks, organizes them into a Tree of Chains, filters noisy chains using a hyperbolic affinity score based on distances in the Poincaré ball, encodes each surviving chain with a Transformer, and produces a final numerical prediction as a weighted combination of per-chain scaled projections. The paper reports that this chain-based design outperforms graph-based and embedding-based baselines across temporal, spatial, and quantity attributes, and that the learned chain weights expose which reasoning paths drive each prediction.

Load-bearing premise

Numerical values can be inferred from the sequence of attribute and relation types alone, without needing to know which specific entities the chain passes through.

Editorial extensions

If this is right

  • Numerical attribute completion in knowledge graphs can be improved by explicitly modeling multi-hop paths; the paper reports MAE reductions of 20.0% on YAGO15K and 7.4% on FB15K-237 over the strongest baselines.
  • Reasoning depth matters: experiments show accuracy improves as chains grow from one to three hops and as chains combine multiple attribute types.
  • Chain weighting makes predictions traceable: for the Coppola birth-date case, four key RA-Chains contribute over 80% of the final prediction.
  • Hyperbolic filtering outperforms Euclidean and random chain selection, and does so even at lower embedding dimensions, indicating that the tree-like chain structure is well matched to hyperbolic geometry.
  • The scaling projection with numerical-aware affine transfer lets the model predict attributes whose value ranges differ by orders of magnitude, from population to height.

Reading between the lines

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

  • Beyond the paper, the RA-Chain representation could be reused as a prompt or planning structure for large language models, since it removes entity literals and isolates the logical pattern of a multi-hop numerical query.
  • The chain-only premise suggests a testable boundary: if entity identity ever matters for a numerical attribute, such as when two entities share the same relation chain but have different values, the method would need an entity-conditioned variant to stay accurate.
  • The hyperbolic affinity scoring mechanism is a general path-pruning tool and could be applied to other graph reasoning tasks, such as link prediction or explainable recommendation, wherever candidate paths grow exponentially with depth.
  • A natural extension is to combine RA-Chains with a lightweight entity-context vector rather than full entity embeddings, which would test whether the paper's entity-free assumption is sufficient or merely a useful approximation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces ChainsFormer, a framework for numerical reasoning over knowledge graphs that reasons over Relation-Attribute Chains (RA-Chains) instead of aggregating graph neighborhoods. RA-Chains are ordered sequences of attribute and relation types (with entity identities removed), retrieved by random walks, filtered via a hyperbolic affinity scoring mechanism, encoded by a Transformer with an in-context representation, and aggregated by an attention-based numerical reasoner. Experiments on FB15K-237 and YAGO15K report MAE improvements of 7.4% and 20.0% over the best included baseline, KGA, along with ablations, hyperparameter studies, and case studies.

Significance. If the empirical results hold, the chain-based perspective is a meaningful contribution to numerical reasoning on knowledge graphs, offering an explicit, step-by-step reasoning structure that graph aggregation methods lack. The paper's strengths include the public release of implementations, detailed ablations that isolate the contribution of the Hyperbolic Filter, Chain Encoder, Numerical-Aware Affine Transfer, and Chain Weighting, and a transparent case study tracing key reasoning chains. The central claim of state-of-the-art performance, however, depends on a comparison set that omits two directly relevant methods cited in the paper itself, and the reported improvements are not accompanied by measures of statistical confidence.

major comments (3)
  1. [Section V-B, Table III; Section II-A] The claim that ChainsFormer 'significantly outperforms' state-of-the-art numerical reasoning methods (Section I and Section V-B) is not supported by the baseline set in Table III. The paper cites NRN [41] and RAKGE [11] in Section II-A as recent numerical reasoning methods, yet neither appears in Table III. Both methods target numerical attribute prediction over knowledge graphs and should be included, or their omission should be justified, to substantiate the stated 7.4% and 20.0% MAE improvements over the true state of the art.
  2. [Section V-A vs. Section IV-F, Eq. (24)] The training objective is described inconsistently: Section V-A states the model is trained with 'L1 loss', while Eq. (24) defines the loss as a mean squared error, and Algorithm 1 refers to Eqs. (22)-(24). This ambiguity directly affects reproducibility. Please specify which loss was used in the experiments and align the text with Eq. (24).
  3. [Section V-B, Table III] The main performance comparison reports a single run per method, with no standard deviations, confidence intervals, or significance tests. Because the margins over the runner-up are small on FB15K-237 (7.4% MAE improvement) and the paper uses the word 'significantly', the results should be accompanied by multiple-seed statistics or a paired test to establish that the differences are not within noise.
minor comments (5)
  1. [Table III] The HyNT Average* RMSE entry for FB15K-237 is reported as 0.66, which appears to be a typo for 0.066; please correct this and verify all entries in the table.
  2. [Eq. (11)] The input sequence P^(0) lists relation tokens in the order e_rl, ..., e_r1, whereas the chain definition in Eq. (5) gives the order (ap, r1, ..., rl, aq). Please make the order consistent or note explicitly if the reversal is intentional.
  3. [Eq. (20)] The notation in C^(0) = |(˜ec1∥...∥˜eck)| + f_len(c1,...,ck) is unclear; please define the absolute-value operation and the length encoding function.
  4. [Section V-A] The text says 'the same spilt with [14]'; this should be 'the same split as [14]'.
  5. [Section V-B] The reported spatial-attribute improvements (30.5% and 9.1%) are stated without specifying the baseline they are relative to; please clarify the reference method for each percentage.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular dependency found; the numerical reasoning pipeline is a normally trained regressor evaluated on external benchmarks.

full rationale

ChainsFormer's derivation chain is empirical and self-contained. RA-Chains (Eq. 5) are a modeling representation, not a fit to the target quantity: the chain c=(ap,r1,...,rl,aq) is an input structure, and the predicted value is produced by learned projections nq=alpha*(np+beta) (Eq. 19) with alpha,beta generated by MLPs from chain embeddings; no equation defines the prediction as equal to an input by construction. The hyperbolic affinity score (Eq. 9) is a ranking/filtering mechanism, not a predictor of nq, and its embeddings are learned jointly with the regression loss (Eq. 24). Hyperparameters lambda, k, Ns are standard model-selection choices, not fitted values that directly determine the reported test MAE. Self-citations ([4], [7], [56]) are background or technical references (recommendation, academic graph, affine projection) and none is load-bearing for the central SOTA claim. The central claim rests on Table III comparisons against external baselines on FB15K-237 and YAGO15K. Although RAKGE and NRN are cited in Section II but absent from Table III, and the HyNT Average* RMSE 0.66 entry appears to be a typo, these are comparison-completeness and reporting concerns, not circularity under the definitions used here.

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

The central claim depends mainly on the RA-Chain representation choice, the sampling and filtering assumptions, and the normalization details. No new physical entities are introduced; the framework is a neural architecture over existing graph structures.

free parameters (5)
  • lambda (Eq. 9) = not reported
    Balances inter-score and intra-score in the hyperbolic affinity score; the paper does not report its value or a sensitivity study for this parameter.
  • top-k filter size = 256
    Number of chains retained by the Hyperbolic Filter; chosen via hyperparameter experiments in Section V-E.
  • random walk count Ns = 2048
    Number of sampled candidate chains for each query; chosen via hyperparameter experiments in Section V-E.
  • Transformer layers Lc = 2
    Number of layers in Chain Encoder and Numerical Reasoner; selected based on experiments shown in Figure 8.
  • hidden dimension d = 256/128
    Hidden dimensions for YAGO15K and FB15K-237; chosen via hyperparameter experiments in Section V-E.
assumptions (4)
  • ad hoc to paper Numerical reasoning rules can be expressed as conjunctions over attributes and relations only, without entity identities (Eq. 4).
    This is the core representation choice of RA-Chains; the paper offers intuition but no proof that entity information is unnecessary for accurate numerical inference.
  • domain assumption Random walks of length at most 3 with 2048 samples per query retrieve sufficient candidate chains (Section V-A).
    The choice is justified by average path lengths around 2.7 to 3.0, but there is no analysis of recall of relevant chains or sensitivity to graph density.
  • domain assumption Attribute normalization for evaluation uses per-attribute min/max ranges that do not leak test information (Eq. 23, Section V-A).
    The paper does not state whether min and max are computed from training splits or the full dataset; if the full dataset is used, normalized metrics are optimistic.
  • standard math Standard Transformer and hyperbolic geometry background, including Möbius addition and log-map, are valid (Section III-B).
    No issue; these are established definitions from cited literature.

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Cite this review

Pith. "Pith review of CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective." pith.science (2026). https://pith.science/paper/G446X6FN

@misc{pith2026250414282,
  author       = {Pith},
  title        = {Pith review of: CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G446X6FN}},
  note         = {Machine review of arXiv:2504.14282}
}
read the original abstract

Reasoning over Knowledge Graphs (KGs) plays a pivotal role in knowledge graph completion or question answering systems, providing richer and more accurate triples and attributes. As numerical attributes become increasingly essential in characterizing entities and relations in KGs, the ability to reason over these attributes has gained significant importance. Existing graph-based methods such as Graph Neural Networks (GNNs) and Knowledge Graph Embeddings (KGEs), primarily focus on aggregating homogeneous local neighbors and implicitly embedding diverse triples. However, these approaches often fail to fully leverage the potential of logical paths within the graph, limiting their effectiveness in exploiting the reasoning process. To address these limitations, we propose ChainsFormer, a novel chain-based framework designed to support numerical reasoning. Chainsformer not only explicitly constructs logical chains but also expands the reasoning depth to multiple hops. Specially, we introduces Relation-Attribute Chains (RA-Chains), a specialized logic chain, to model sequential reasoning patterns. ChainsFormer captures the step-by-step nature of multi-hop reasoning along RA-Chains by employing sequential in-context learning. To mitigate the impact of noisy chains, we propose a hyperbolic affinity scoring mechanism that selects relevant logic chains in a variable-resolution space. Furthermore, ChainsFormer incorporates an attention-based numerical reasoner to identify critical reasoning paths, enhancing both reasoning accuracy and transparency. Experimental results demonstrate that ChainsFormer significantly outperforms state-of-the-art methods, achieving up to a 20.0% improvement in performance. The implementations are available at https://github.com/zhaodazhuang2333/ChainsFormer.

Figures

Figures reproduced from arXiv: 2504.14282 by the authors.

Figure 1
Figure 1. An example of numerical reasoning: predicting missing numerical [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Average number of logic chains connected to each query in YAGO15K [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The overall architecture of ChainsFormer is shown in (A). ChainsFormer has for key components: Query Retrieval, Hyperbolic Filter, Chain Encoder [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Performance comparison across different reasoning settings. The [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: A case study for ChainsFormer’s reasoning process on Francis Ford Coppola’s birth date. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: A case study on visualization of the effect of Hyperbolic Filter on [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Performance comparison on FB15K Dataset of filtering across [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Hyperparameter study on YAGO15K and FB15K-237, exploring effects of retrieval numbers [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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Reference graph

Works this paper leans on

61 extracted references · 54 canonical work pages

  1. [41]

    Knowledge graph reasoning over entities and numerical values

    Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Bing Yin, and Yangqiu Song. Knowledge graph reasoning over entities and numerical values. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023, pages 57–68, 2023

    work page 2023

  2. [11]

    Exploiting relation-aware attribute representation learning in knowledge graph embedding for numerical reasoning

    Gayeong Kim, Sookyung Kim, Ko Keun Kim, Suchan Park, Heesoo Jung, and Hogun Park. Exploiting relation-aware attribute representation learning in knowledge graph embedding for numerical reasoning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023, pages 1086–1096, 2023

    work page 2023

  3. [1]

    Rotate: Knowledge graph embedding by relational rotation in complex space

    Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 , 2019

    work page 2019

  4. [2]

    Towards foundation models for knowledge graph reasoning

    Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, and Zhaocheng Zhu. Towards foundation models for knowledge graph reasoning. In The Twelfth International Conference on Learning Repre- sentations, 2024

    work page 2024

  5. [3]

    Muthukrishnan, Gerard de Melo, and Yongfeng Zhang

    Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. Reinforcement knowledge graph reasoning for ex- plainable recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, pages 285–294, 2019

    work page 2019

  6. [4]

    Editkg: Editing knowledge graph for recommenda- tion

    Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, and Chenghu Zhou. Editkg: Editing knowledge graph for recommenda- tion. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages 112–122, 2024

    work page 2024

  7. [5]

    A survey on complex knowledge base question answering: Methods, challenges and solutions

    Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen. A survey on complex knowledge base question answering: Methods, challenges and solutions. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021 , pages 4483– 4491, 2021

    work page 2021

  8. [6]

    Knowledge graph embedding based question answering

    Xiao Huang, Jingyuan Zhang, Dingcheng Li, and Ping Li. Knowledge graph embedding based question answering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019 , pages 105–113, 2019

    work page 2019

Show all 61 references
  1. [7]

    Acemap: Knowledge discovery through academic graph

    Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen, Guanjie Zheng, Jiaxin Ding, Liyao Xiang, Nanyang Ye, Meng Jin, Shiyu Liang, et al. Acemap: Knowledge discovery through academic graph. arXiv preprint arXiv:2403.02576, 2024

    2024 arXiv

  2. [8]

    A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal

    Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal. IEEE Trans. Pattern Anal. Mach. Intell., 46(12):9456– 9478, 2024

    2024

  3. [9]

    Ni, Heung-Yeung Shum, and Jian Guo

    Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, and Jian Guo. Think- on-graph: Deep and responsible reasoning of large language model on knowledge graph. In The Twelfth International Conference on Learning Represen...

    2024

  4. [10]

    Introducing semantic infor- mation for numerical attribute prediction over knowledge graphs

    Bingcong Xue, Yanzeng Li, and Lei Zou. Introducing semantic infor- mation for numerical attribute prediction over knowledge graphs. In The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings , volume 13489 of L...

    2022

  5. [12]

    Phan, and Siu Cheung Hui

    Yi Tay, Luu Anh Tuan, Minh C. Phan, and Siu Cheung Hui. Multi- task neural network for non-discrete attribute prediction in knowledge graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017, pag...

    2017

  6. [13]

    Node attribute completion in knowledge graphs with multi-relational propagation

    Eda Bayram, Alberto Garc ´ıa-Dur´an, and Robert West. Node attribute completion in knowledge graphs with multi-relational propagation. In IEEE International Conference on Acoustics, Speech and Signal Pro- cessing, ICASSP 2021, Toronto, ON, Canada, June 6-11, 2021 , pages 3590–...

    2021

  7. [14]

    Szekely, and Ke-Thia Yao

    Jiang Wang, Filip Ilievski, Pedro A. Szekely, and Ke-Thia Yao. Aug- menting knowledge graphs for better link prediction. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , pages 2277–2283, 2022

    2022

  8. [15]

    Represen- tation learning on hyper-relational and numeric knowledge graphs with transformers

    Chanyoung Chung, Jaejun Lee, and Joyce Jiyoung Whang. Represen- tation learning on hyper-relational and numeric knowledge graphs with transformers. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-...

    2023

  9. [16]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural infor- mation processing systems , 35:24824–24837, 2022

    2022

  10. [17]

    Learn to explain: Multimodal reasoning via thought chains for science ques- tion answering

    Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song- Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science ques- tion answering. Advances in Neural Information Processing Systems , 35:250...

    2022

  11. [18]

    A survey of chain of thought reasoning: Advances, frontiers and future

    Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, and Ting Liu. A survey of chain of thought reasoning: Advances, frontiers and future. arXiv preprint arXiv:2309.15402 , 2023

    2023 arXiv

  12. [19]

    Le, Ed H

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V . Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self- consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigal...

    2023

  13. [20]

    Towards revealing the mystery behind chain of thought: a theoretical perspective

    Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang. Towards revealing the mystery behind chain of thought: a theoretical perspective. Advances in Neural Information Processing Systems, 36, 2024

    2024

  14. [21]

    A review: Knowledge reasoning over knowledge graph

    Xiaojun Chen, Shengbin Jia, and Yang Xiang. A review: Knowledge reasoning over knowledge graph. Expert systems with applications , 141:112948, 2020

    2020

  15. [22]

    Empowering language models with knowledge graph reasoning for open-domain question answering

    Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chen- guang Zhu, Kai-Wei Chang, and Yizhou Sun. Empowering language models with knowledge graph reasoning for open-domain question answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Langua...

    2022

  16. [23]

    Translating embeddings for modeling multi- relational data

    Antoine Bordes, Nicolas Usunier, Alberto Garc ´ıa-Dur´an, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi- relational data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems

  17. [24]

    Fan Yang, Zhilin Yang, and William W. Cohen. Differentiable learning of logical rules for knowledge base reasoning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA...

    2017

  18. [25]

    Rlogic: Recursive logical rule learning from knowledge graphs

    Kewei Cheng, Jiahao Liu, Wei Wang, and Yizhou Sun. Rlogic: Recursive logical rule learning from knowledge graphs. In Aidong Zhang and Huzefa Rangwala, editors, KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, ...

    2022

  19. [26]

    Ahmed, and Yizhou Sun

    Kewei Cheng, Nesreen K. Ahmed, and Yizhou Sun. Neural composi- tional rule learning for knowledge graph reasoning. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 , 2023

    2023

  20. [27]

    Knowledge graph embedding by translating on hyperplanes

    Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI conference on artificial intelligence , volume 28, 2014

    2014

  21. [28]

    Learning entity and relation embeddings for knowledge graph comple- tion

    Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph comple- tion. In Proceedings of the AAAI conference on artificial intelligence , volume 29, 2015

    2015

  22. [29]

    Embedding entities and relations for learning and inference in knowledge bases

    Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the International Conference on Learning Representations (ICLR) 2015 , 2015

    2015

  23. [30]

    Complex embeddings for simple link prediction

    Th ´eo Trouillon, Johannes Welbl, Sebastian Riedel, ´Eric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In International conference on machine learning , pages 2071–2080. PMLR, 2016

    2016

  24. [31]

    Kipf and Max Welling

    Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , 2017

    2017

  25. [32]

    Heteroge- neous graph transformer

    Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. Heteroge- neous graph transformer. In WWW ’20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020 , pages 2704–2710, 2020

    2020

  26. [33]

    Taluk- dar

    Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Taluk- dar. Composition-based multi-relational graph convolutional networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 , 2020

    2020

  27. [34]

    Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, and Jian Tang. Neural bellman-ford networks: A general graph neural network framework for link prediction. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Syst...

    2021

  28. [35]

    Mmkgr: Multi-hop multi-modal knowledge graph reason- ing

    Shangfei Zheng, Weiqing Wang, Jianfeng Qu, Hongzhi Yin, Wei Chen, and Lei Zhao. Mmkgr: Multi-hop multi-modal knowledge graph reason- ing. In 2023 IEEE 39th International Conference on Data Engineering (ICDE), pages 96–109. IEEE, 2023

    2023

  29. [36]

    Hybrid transformer with multi-level fusion for multimodal knowledge graph completion

    Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, and Huajun Chen. Hybrid transformer with multi-level fusion for multimodal knowledge graph completion. In Proceedings of the 45th international ACM SIGIR conference on research and de...

    2022

  30. [37]

    Knowledge graph embedding with numeric attributes of entities

    Yanrong Wu and Zhichun Wang. Knowledge graph embedding with numeric attributes of entities. In Proceedings of The Third Workshop on Representation Learning for NLP , Rep4NLP@ACL 2018, Melbourne, Australia, July 20, 2018 , pages 132–136, 2018

    2018

  31. [38]

    MARINE: multi-relational network embeddings with relational proximity and node attributes

    Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh, and Shou- De Lin. MARINE: multi-relational network embeddings with relational proximity and node attributes. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019 , pages 470– 479, 2019

    2019

  32. [39]

    Incorporating literals into knowledge graph embeddings

    Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, and Asja Fischer. Incorporating literals into knowledge graph embeddings. In The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedi...

    2019

  33. [40]

    Incorpo- rating attributes semantics into knowledge graph embeddings

    Mingyang Li, Neng Gao, Chenyang Tu, Jia Peng, and Min Li. Incorpo- rating attributes semantics into knowledge graph embeddings. In 24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021, Dalian, China, May 5-7, 2021 , pages 620–625, 2021

    2021

  34. [42]

    Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro A. Szekely. Rep- resenting numbers in NLP: a survey and a vision. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL- HLT...

    2021

  35. [43]

    Learning numerical at- tributes in knowledge bases

    Bhushan Kotnis and Alberto Garc ´ıa-Dur´an. Learning numerical at- tributes in knowledge bases. In 1st Conference on Automated Knowledge Base Construction, AKBC 2019, Amherst, MA, USA, May 20-22, 2019 , 2019

    2019

  36. [44]

    Hyperbolic deep neural networks: A survey

    Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, and Guoying Zhao. Hyperbolic deep neural networks: A survey. IEEE Trans. Pattern Anal. Mach. Intell. , 44(12):10023–10044, 2022

    2022

  37. [45]

    Hy- perbolic graph neural networks: A tutorial on methods and applications

    Min Zhou, Menglin Yang, Bo Xiong, Hui Xiong, and Irwin King. Hy- perbolic graph neural networks: A tutorial on methods and applications. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023, p...

    2023

  38. [46]

    Hyperbolic neural networks

    Octavian Ganea, Gary B ´ecigneul, and Thomas Hofmann. Hyperbolic neural networks. Advances in neural information processing systems , 31, 2018

    2018

  39. [47]

    Hyperbolic graph neural networks

    Qi Liu, Maximilian Nickel, and Douwe Kiela. Hyperbolic graph neural networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , pages 8228–8239, 2019

    2019

  40. [48]

    A hyperbolic- to-hyperbolic graph convolutional network

    Jindou Dai, Yuwei Wu, Zhi Gao, and Yunde Jia. A hyperbolic- to-hyperbolic graph convolutional network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 154–163, June 2021

    2021

  41. [49]

    Nurendra Choudhary, Nikhil Rao, and Chandan K. Reddy. Hyperbolic graph neural networks at scale: A meta learning approach. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, U...

    2023

  42. [50]

    Hospedales

    Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. Multi- relational poincar ´e graph embeddings. In Advances in Neural Informa- tion Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Van- couver, BC...

    2019

  43. [51]

    Low-dimensional hyperbolic knowledge graph embeddings

    Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, and Christopher R ´e. Low-dimensional hyperbolic knowledge graph embeddings. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 6...

    2020

  44. [52]

    Geometry interaction knowledge graph embeddings

    Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. Geometry interaction knowledge graph embeddings. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intel- ligence,...

    2022

  45. [53]

    Poincar ´e embeddings for learning hierarchical representations

    Maximillian Nickel and Douwe Kiela. Poincar ´e embeddings for learning hierarchical representations. Advances in neural information processing systems, 30, 2017

    2017

  46. [54]

    Gomez, Lukasz Kaiser, and Illia Polosukhin

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, De...

    2017

  47. [55]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV , USA, June 27-30, 2016 , pages 770–778, 2016

    2016

  48. [56]

    Oxygenerator: Reconstructing global ocean deoxygenation over a century with deep learning

    Bin Lu, Ze Zhao, Luyu Han, Xiaoying Gan, Yuntao Zhou, Lei Zhou, Lu- oyi Fu, Xinbing Wang, Chenghu Zhou, and Jing Zhang. Oxygenerator: Reconstructing global ocean deoxygenation over a century with deep learning. In Forty-first International Conference on Machine Learning, ICML ...

    2024

  49. [57]

    Observed versus latent features for knowledge base and text inference

    Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, July 26-31, 2015 , pages 57–66, 2015

    2015

  50. [58]

    Learning sequence encoders for temporal knowledge graph completion

    Alberto Garc ´ıa-Dur´an, Sebastijan Dumancic, and Mathias Niepert. Learning sequence encoders for temporal knowledge graph completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, p...

    2018

  51. [59]

    Rosenblum

    Ye Liu, Hui Li, Alberto Garc ´ıa-Dur´an, Mathias Niepert, Daniel O ˜noro- Rubio, and David S. Rosenblum. MMKG: multi-modal knowledge graphs. In The Semantic Web - 16th International Conference, ESWC 2019, Portoroˇz, Slovenia, June 2-6, 2019, Proceedings , volume 11503 of Lectu...

    2019

  52. [60]

    Bollacker, Colin Evans, Praveen K

    Kurt D. Bollacker, Colin Evans, Praveen K. Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In Jason Tsong-Li Wang, editor, Proceed- ings of the ACM SIGMOD International Conference on Management of Data...

    2008

  53. [2013]

    Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States , pages 2787–2795, 2013

    2013

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.