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DuCape: Dual Quaternion and Capsule Network–Based Temporal Knowledge Graph Embedding by Sensen Zhang; Xun Liang; Hui Tang; Xiangping Zheng; Alex X. Zhang; Yuefeng Ma is a Computer Science article available to read on EtoBox.

What is DuCape: Dual Quaternion and Capsule Network–Based Temporal Knowledge Graph Embedding about?

In recent years, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can only capture some of them with insufficient modeling capacity, and none have a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this paper, we propose a more powerful KGE framework named DuCape , which for the first time, combines dual quaternion and capsule network in modeling to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k-dimensional rigid transformation parametrized by relation and temporal, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and temporal trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic data sets show that the DuCape model constructed in this paper is superior to existing state-of-the-art models.

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Author

Sensen Zhang; Xun Liang; Hui Tang; Xiangping Zheng; Alex X. Zhang; Yuefeng Ma

Publisher

ACM

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)