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Article

BiQCap: A Biquaternion and Capsule Network-Based Embedding Model for Temporal Knowledge Graph Completion

Authors:

Xiangping Zheng,

Bo WuAuthors Info & Claims

Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II

Pages 673 - 688

https://doi.org/10.1007/978-3-031-30672-3_45

Published: 17 April 2023 Publication History

Abstract

Temporal Knowledge Graphs (TKGs) provide a temporal context for facts, capturing temporal information and the dynamic nature of actual world facts. However, typical TKGs often suffer from incomplete dynamics with missing facts in real-world scenarios. Temporal Knowledge Graph Embedding (TKGE) is one of the critical approaches to tackling the challenge. However, the existing TKGE models are weak in simultaneously representing hierarchical semantics and other relation patterns. Therefore, embedding TKGs in a single space, no matter the Euclidean space, or hyperbolic space, cannot capture the complex structures of TKGs accurately. In addition, few existing models have a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this paper, we propose a new TKGE model,

BiQCap

, which for the first time, combines biquaternion and capsule network in modeling to make up for the defects of existing TKGE models. BiQCap represents each temporal entity as a translation and each relation as euclidean rotation and hyperbolic rotation in biquaternions vector space. Further, we employ the embeddings of entities, relations, and temporal trained from biquaternions as the input to capsule networks. Experimental results on five well-known benchmark datasets show that our BiQCap achieves state-of-the-art performance.

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Cited By

Mei XYang LJiang ZCai XGao DHan JPan S(2024)An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graphNeural Networks10.1016/j.neunet.2024.106219174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106219
Zhang SLiang XTang HGuan ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Hybrid Interaction Temporal Knowledge Graph Embedding Based on Householder TransformationsProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613446(8954-8962)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613446

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II

Apr 2023

836 pages

ISBN:978-3-031-30671-6

DOI:10.1007/978-3-031-30672-3

Editors:
Xin Wang
Tianjin University, Tianjin, China
,
Maria Luisa Sapino
University of Torino, Turin, Italy
,
Wook-Shin Han
POSTECH, Pohang, Korea (Republic of)
,
Amr El Abbadi
University of California Santa Barbara, Santa Barbara, CA, USA
,
Gill Dobbie
University of Auckland, Auckland, New Zealand
,
Zhiyong Feng
Tianjin University, Tianjin, China
,
Yingxiao Shao
Beijing University of Posts and Telecommunications, Beijing, China
,
Hongzhi Yin
The University of Queensland, Brisbane, QLD, Australia

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 April 2023

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Cited By

Mei XYang LJiang ZCai XGao DHan JPan S(2024)An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graphNeural Networks10.1016/j.neunet.2024.106219174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106219
Zhang SLiang XTang HGuan ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Hybrid Interaction Temporal Knowledge Graph Embedding Based on Householder TransformationsProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613446(8954-8962)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613446

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