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

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

February 1, 2023

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Authors

Ren Li

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


Yanan Cao

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


Qiannan Zhu

Gaoling School of Artificial Intelligence, Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods


Guanqun Bi

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


Fang Fang

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


Yi Liu

National Computer Network Emergency Response Technical Team/Coordination Center of China


Qian Li

University of Technology Sydney


DOI:

10.1609/aaai.v36i5.20521


Abstract:

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://github.com/renli1024/SE-GNN.

Topics: AAAI

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HOW TO CITE:

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View Proceedings of the AAAI Conference on Artificial Intelligence (2022) 5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View AAAI 2022, 5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li (2022). How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View. Proceedings of the AAAI Conference on Artificial Intelligence, 5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. 2022. How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View. "Proceedings of the AAAI Conference on Artificial Intelligence". 5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. (2022) "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View", Proceedings of the AAAI Conference on Artificial Intelligence, p.5781-5791

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li, "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View", AAAI, p.5781-5791, 2022.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 5781-5791.

Ren Li||Yanan Cao||Qiannan Zhu||Guanqun Bi||Fang Fang||Yi Liu||Qian Li. How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View. AAAI[Internet]. 2022[cited 2023]; 5781-5791.


ISSN: 2374-3468


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