A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network-Reference-Cited by-同舟云学术

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A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network

Published:2024-03-12 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Guo Hui¹,Yang Chengyong²,Zhou Liqing²,Wei Shiwei³

Affiliation:

1. School of Information Science and Engineering, Guilin University of Technology, Guilin, People’s Republic of China

2. Network and Information Center, Guilin University of Technology, Guilin, People’s Republic of China

3. School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, People’s Republic of China

Funder

National Natural Science Foundation of China

National Social Science Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2024.2327441

Reference31 articles.

1. Balažević I. Allen C. & Hospedales T. M. (2019a September 17–19). Hypernetwork knowledge graph embeddings//artificial neural networks and machine learning–ICANN 2019. Workshop and special sessions: 28th international conference on artificial neural networks Munich Germany Proceedings 28. Springer International Publishing (pp. 553–565).

2. Balažević I. Allen C. & Hospedales T. M. (2019b). Tucker: Tensor factorization for Knowledge Graph completion. arXiv preprint arXiv:1901.09590.

3. Multi-relational poincaré graph embeddings;Balazevic I.;Advances in Neural Information Processing Systems,2019

4. Cao X. Shi Y. Wang J. Yu H. Wang X. & Yan Z. (2022). Cross-modal knowledge graph contrastive learning for machine learning method recommendation. Proceedings of the 30th ACM International Conference on Multimedia (pp. 3694–3702).

5. Chami I. Wolf A. Juan D. C. Sala F. Ravi S. & Ré C. (2020). Low-dimensional hyperbolic Knowledge Graph embeddings. arXiv preprint arXiv:2005.00545.

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