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RA-GCN: Relational Aggregation Graph Convolutional Network for Knowledge Graph Completion

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Zhiqiang ZhanAuthors Info & Claims

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

Pages 580 - 586

https://doi.org/10.1145/3383972.3384067

Published: 26 May 2020 Publication History

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Abstract

Knowledge graphs display various entities and their relationships in the real world based on knowledge representation and can analyze and predict the intrinsic relationship between knowledge embeddings through data mining and information processing, which are widely used in search engines, web analytics and smart recommendation areas. As existing knowledge graph information is continuously created and grown, it is a curial task to determine whether the information in knowledge graph is correct and to complete the missing information. In response to this challenge, the researchers proposed a number of graph convolutional network (GCN)-based models to characterize knowledge graphs. The state-of-the-art model is R-GCN, which can effectively extract features. This paper deeply studies the algorithm ideas and results of the R-GCN model, explores whether the model can be further optimized in entity classification and link prediction, and finally, improves the original model and proposes a relational aggregation graph convolutional network. Specifically, this paper finds that a subset of the set of entities may be directly connected to a central entity. All the entities in this subset possess partially identical attributes. At the same time, the relationships between these entities and the central entity may be similar. These similar attributes and relationships can be abstractly aggregated into virtual entities and virtual relationships, respectively, to better extract the topological relationship features. This paper uses the FB15k dataset to evaluate the performance of the proposed model on knowledge graph completion tasks. The experimental results show that the proposed RA-GCN model achieves a certain level improvement compared with the original model and that it can extract knowledge graph topological relationship characteristics more effectively.

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Index Terms

RA-GCN: Relational Aggregation Graph Convolutional Network for Knowledge Graph Completion
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

February 2020

607 pages

ISBN:9781450376426

DOI:10.1145/3383972

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Shenzhen University: Shenzhen University

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Published: 26 May 2020

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ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing

February 15 - 17, 2020

Shenzhen, China

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Yu SXia FWang YLi SFebrinanto FChetty M(2024)PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level ForecastingIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322967111:1(717-730)Online publication date: Feb-2024
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Wu YYang JWang LXu Z(2024)Graph-Aware Multi-View Fusion for Rumor Detection on Social MediaICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446777(9961-9965)Online publication date: 14-Apr-2024
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Wang YLi QZhang YZhang XJu ZSun J(2023)A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model2023 8th International Conference on Data Science in Cyberspace (DSC)10.1109/DSC59305.2023.00017(44-51)Online publication date: 18-Aug-2023
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Knowledge Embedding Based Graph Convolutional Network

Knowledge Graph Augmentation with Entity Identification for Improving Knowledge Graph Completion Performance

A Comprehensive Survey on Automatic Knowledge Graph Construction

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