Relational Message Passing for Fully Inductive Knowledge Graph Completion-Reference-Cited by-同舟云学术

Relational Message Passing for Fully Inductive Knowledge Graph Completion

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 39th International Conference on Data Engineering (ICDE)
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Author:

Geng Yuxia¹,Chen Jiaoyan²,Pan Jeff Z.³,Chen Mingyang¹,Jiang Song⁴,Zhang Wen⁵,Chen Huajun⁵

Affiliation:

1. Zhejiang University,College of Computer Science and Technology,Hangzhou,China

2. The University of Manchester,Department of Computer Science,Manchester,United Kingdom

3. School of Informatics The University of Edinburgh,Edinburgh,United Kingdom

4. NAIE PDU Huawei Technologies Co., Ltd.,Xi’an,China

5. Donghai Laboratory,Zhoushan,China

Publisher

IEEE

Link

Reference47 articles.

4. Composition-based multi-relational graph convolutional networks;vashishth;ICLR OpenReview net,2020

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3. Causal Subgraph Learning for Generalizable Inductive Relation Prediction;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24