From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding

Article


Cai, Borui, Xiang, Yong, Gao, Longxiang, Wu, Di, Zhang, He, Jin, Jiong and Luan, Tom. 2024. "From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding." IEEE Transactions on Knowledge and Data Engineering. https://doi.org/DOI:10.1109/TKDE.2024.3437479
Article Title

From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding

ERA Journal ID17876
Article CategoryArticle
AuthorsCai, Borui, Xiang, Yong, Gao, Longxiang, Wu, Di, Zhang, He, Jin, Jiong and Luan, Tom
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Number of Pages7
Year2024
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/DOI:10.1109/TKDE.2024.3437479
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/10636956
Abstract

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.

KeywordsKnowledge graph embedding; deep neural network; parameter-efficiency; representation learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460506. Graph, social and multimedia data
4602. Artificial intelligence
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsDeakin University
Qilu University of Technology, China
School of Mathematics, Physics and Computing
CNPIEC KEXIN, China
Swinburne University of Technology
Xi'an Jiaotong University, China
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