DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Han, Zhen; Chen, Peng; Ma, Yunpu; Tresp, Volker

doi:10.18653/v1/2020.emnlp-main.593

Cited by 47 publications

(45 citation statements)

References 20 publications

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“…Extensive studies have been done for temporal KG completion task (Leblay and Chekol, 2018;García-Durán et al, 2018;Goel et al, 2020;Han et al, 2020a). Besides, a line of work (Trivedi et al, 2017;Jin et al, 2019;Deng et al, 2020;Zhu et al, 2020) has been proposed for the tKG forecasting task and can generalize to unseen timestamps.…”

Section: Temporal Knowledge Graph Reasoningmentioning

confidence: 99%

Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

Han¹,

Ding²,

Ma³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model's superior performance on the future link forecasting task.

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Section: Temporal Knowledge Graph Reasoningmentioning

confidence: 99%

Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

Han¹,

Ding²,

Ma³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Temporal KG Reasoning. Reasoning on temporal KG can broadly be categorized into two settings, interpolation (Sadeghian et al, 2016;García-Durán et al, 2018;Leblay and Chekol, 2018;Dasgupta et al, 2018;Wu et al, 2019;Xu et al, 2020;Goel et al, 2020;Wu et al, 2020;Han et al, 2020a;Jung et al, 2020) and extrapolation (Trivedi et al, 2017(Trivedi et al, , 2018Han et al, 2020b;Deng et al, 2020;Jin et al, 2019Jin et al, , 2020Zhu et al, 2020;Li et al, 2021), as mentioned in Jin et al (2020). Under the former setting, models attempt to infer missing facts at historical timestamps.…”

Section: Related Workmentioning

confidence: 99%

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Li¹,

Jin²,

Guan³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.

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“…The scoring function is similar to the one used in Balažević et al (2019a) and Han et al (2020) defined as:…”

Section: Hyperbolic Geometrymentioning

confidence: 99%

Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures

Montella¹,

Barahona²,

Heinecke

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task. The main limitation of such models is their insensitivity to time. Indeed, the temporal aspect of stored facts is often ignored. To this end, more and more works consider time as a parameter to complete KGs. In this paper, we first demonstrate that, by simply increasing the number of negative samples, the recent ATTH model can achieve competitive or even better performance than the state-of-the-art on Temporal KGs (TKGs), albeit its nontemporality. We further propose HERCULES, a time-aware extension of ATTH model, which defines the curvature of a Riemannian manifold as the product of both relation and time. Our experiments show that both HERCULES and ATTH achieve competitive or new state-of-the-art performances on ICEWS04 and ICEWS05-15 datasets. Therefore, one should raise awareness when learning TKGs representations to identify whether time truly boosts performances.

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DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Cited by 47 publications

References 20 publications

Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures

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