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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

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Wu, Jiapeng, Cao, Meng, Cheung, Jackie Chi Kit, Hamilton, William L.·2020·Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI 10.18653/v1/2020.emnlp-main.462

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Abstract

Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task.Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations.However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions.Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs.We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques.Experiments 1 on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks.Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

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Authors: Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L.
Affiliations: Mila - Quebec Artificial Intelligence Institute (CA)
ORCID: Wu, Jiapeng
Cao, Meng
Hamilton, William L.
Venue: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Publisher: Association for Computational Linguistics
Published: 2020
Pages: 5730-5746
DOI: 10.18653/v1/2020.emnlp-main.462
OpenAlex: W3099845049
Cited by: 167
References: 43
Citations by year: 2026: 23 · 2025: 31 · 2024: 39 · 2023: 39 · 2022: 24 · 2021: 11
FWCI: 7.07
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Canadian Institute for Advanced Research
Compute Canada
Samsung
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords: temporal knowledge graphs, knowledge graph completion, graph neural networks, missing data imputation, temporal sparsity

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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

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