Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network

Authors

Guanzheng Chen, Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing
Jinyuan Fang, Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing
Zaiqiao Meng, University of Glasgow
Qiang Zhang, Zhejiang University
Shangsong Liang, Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analyis and Processing & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022

Abstract

Learning effective representations of entities and relations for knowledge graphs (KGs) is critical to the success of many multi-relational learning tasks. Existing methods based on graph neural networks learn a deterministic embedding function, which lacks sufficient flexibility to explore better choices when dealing with the imperfect and noisy KGs such as the scarce labeled nodes and noisy graph structure. To this end, we propose a novel multi-relational graph Gaussian Process network (GGPN), which aims to improve the flexibility of deterministic methods by simultaneously learning a family of embedding functions, i.e., a stochastic embedding function. Specifically, a Bayesian Gaussian Process (GP) is proposed to model the distribution of this stochastic function and the resulting representations are obtained by aggregating stochastic function values, i.e., messages, from neighboring entities. The two problems incurred when leveraging GP in GGPN are the proper choice of kernel function and the cubic computational complexity. To address the first problem, we further propose a novel kernel function that can explicitly take the diverse relations between each pair of entities into account and be adaptively learned in a data-driven way. We address the second problem by reformulating GP as a Bayesian linear model, resulting in a linear computational complexity. With these two solutions, our GGPN can be efficiently trained in an end-to-end manner. We evaluate our GGPN in link prediction and entity classification tasks, and the experimental results demonstrate the superiority of our method. Our code is available at https://github.com/sysu-gzchen/GGPN.

First Page

5530

Last Page

5538

DOI

10.1609/aaai.v36i5.20492

Publication Date

6-28-2022

Keywords

Knowledge Representation And Reasoning (KRR), Machine Learning (ML)

Comments

IR conditions: non-described

Recommended Citation

G. Chen, J. Fang, Z. Meng, Q. Zhang, and S. Liang, “Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network”, AAAI, vol. 36, no. 5, pp. 5530-5538, Jun. 2022, doi:10.1609/aaai.v36i5.20492