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Open Access

A Temporal Knowledge Graph Embedding Model Based on Variable Translation

Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China and also with Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China
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Abstract

Knowledge representation learning (KRL) aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors. It is popularly used in knowledge graph completion (or link prediction) tasks. Translation-based knowledge representation learning methods perform well in knowledge graph completion (KGC). However, the translation principles adopted by these methods are too strict and cannot model complex entities and relationships (i.e., N-1, 1-N, and N-N) well. Besides, these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts. Therefore, we propose a temporal knowledge graph embedding model based on variable translation (TKGE-VT). The model proposes a new variable translation principle, which enables flexible transformation between entities and relationship embedding. Meanwhile, this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs. We conduct link prediction and triplet classification experiments on four benchmark datasets: WN11, WN18, FB13, and FB15K. Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.

Keywords

knowledge graphknowledge graph completionvariable translationtemporal propertieslink predictiontriplet classification

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Tsinghua Science and Technology
Volume 29 Issue 5,
October 2024
Pages 1554-1565
DOI: 10.26599/TST.2023.9010142
Cite this article:
Han Y, Lu G, Zhang S, et al. A Temporal Knowledge Graph Embedding Model Based on Variable Translation. Tsinghua Science and Technology, 2024, 29(5): 1554-1565. https://doi.org/10.26599/TST.2023.9010142

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Received: 20 June 2023
Revised: 13 October 2023
Accepted: 21 November 2023
Published: 02 May 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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