Improving Temporal Knowledge Graph Completion via Tensor Decomposition with Relation-Time Context and Multi-Time Perspective

Nam Le, Nam Le, Thanh Le, Thanh Le, Bac Le, Bac Le

2025

Abstract

Knowledge graphs have progressively incorporated temporal dimensions to effectively mirror the dynamism of real-world data, proving instrumental in applications ranging from question answering to event prediction. While the ubiquity of data incompleteness and well-established challenges of traditional knowledge graph embedding techniques remain acknowledged, this paper propels the frontier of this research area. We introduce Multi-Time Perspective Relation-Time Context ComplEx Embedding (MPComplEx), a tensor decomposition-based completion temporal knowledge graph model that not only assimilates temporal and relational interactions specific to timestamps but also integrates advanced time perspective features from the recent TPComplEx models. Our experimental evaluations illustrate dramatic enhancements over conventional models, achieving state-of-the-art performance on benchmark datasets with notable increments: 4.30%/4.79% on ICEWS-14, 11.70%/11.48% on ICEWS-05-15, 21.50%/31.20% on YAGO15k, and 26.90%/66.09% on GDELT in term of absolute/relative performance gains on mean reciprocal rank (MMR).

Paper Citation

in Harvard Style

Le N., Le T. and Le B. (2025). Improving Temporal Knowledge Graph Completion via Tensor Decomposition with Relation-Time Context and Multi-Time Perspective. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 326-333. DOI: 10.5220/0013130500003890

in Bibtex Style

@conference{icaart25,
author={Nam Le and Thanh Le and Bac Le},
title={Improving Temporal Knowledge Graph Completion via Tensor Decomposition with Relation-Time Context and Multi-Time Perspective},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={326-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013130500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Improving Temporal Knowledge Graph Completion via Tensor Decomposition with Relation-Time Context and Multi-Time Perspective
SN - 978-989-758-737-5
AU - Le N.
AU - Le T.
AU - Le B.
PY - 2025
SP - 326
EP - 333
DO - 10.5220/0013130500003890
PB - SciTePress