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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 5: AAAI-22 Technical Tracks 5

Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network

February 1, 2023

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Authors

Guanzheng Chen

School of Computer Science and Engineering, Sun Yat-sen University, China Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China


Jinyuan Fang

School of Computer Science and Engineering, Sun Yat-sen University, China Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China


Zaiqiao Meng

School of Computing Science, University of Glasgow, United Kingdom


Qiang Zhang

Hangzhou Innovation Center, Zhejiang University, China College of Computer Science and Technology, Zhejiang University, China


Shangsong Liang

School of Computer Science and Engineering, Sun Yat-sen University, China Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates


DOI:

10.1609/aaai.v36i5.20492


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.

Topics: AAAI

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HOW TO CITE:

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network Proceedings of the AAAI Conference on Artificial Intelligence (2022) 5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network AAAI 2022, 5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang (2022). Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network. Proceedings of the AAAI Conference on Artificial Intelligence, 5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. 2022. Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network. "Proceedings of the AAAI Conference on Artificial Intelligence". 5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. (2022) "Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network", Proceedings of the AAAI Conference on Artificial Intelligence, p.5530-5538

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang, "Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network", AAAI, p.5530-5538, 2022.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. "Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. "Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 5530-5538.

Guanzheng Chen||Jinyuan Fang||Zaiqiao Meng||Qiang Zhang||Shangsong Liang. Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network. AAAI[Internet]. 2022[cited 2023]; 5530-5538.


ISSN: 2374-3468


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Copyright 2022, Association for the Advancement of
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