A structure-enhanced generative adversarial network for knowledge graph zero-shot relational learning-Reference-Cited by-同舟云学术

A structure-enhanced generative adversarial network for knowledge graph zero-shot relational learning

Published:2023-06 Issue: Volume:629 Page:169-183
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Li Xuewei,Ma Jinming,Yu Jian,Zhao Mankun,Yu Mei,Liu Hongwei,Ding Weiping^ORCID,Yu Ruiguo

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. Improving multi-hop question answering over knowledge graphs using knowledge base embeddings;Saxena,2020

2. Jointly non-sampling learning for knowledge graph enhanced recommendation;Chen,2020

3. Target inductive methods for zero-shot regression;Fdez-Díaz;Inf. Sci.,2022

4. Kernelized distance learning for zero-shot recognition;Zarei;Inf. Sci.,2021

5. Inductive link prediction for nodes having only attribute information;Hao,2020

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