Knowledge Graph-Based Reinforcement Federated Learning for Chinese Question and Answering-Reference-Cited by-全球学者库

Knowledge Graph-Based Reinforcement Federated Learning for Chinese Question and Answering

Published:2024-02 Issue:1 Volume:11 Page:1035-1045
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Xu Liang¹^ORCID,Chen Tao²^ORCID,Hou Zhaoxiang³,Zhang Weishan²^ORCID,Hon Chitin⁴,Wang Xiao⁵,Wang Di⁴,Chen Long⁶^ORCID,Zhu Wenyin⁷,Tian Yunlong⁷,Ning Huansheng¹^ORCID,Wang Fei-Yue⁸^ORCID

Affiliation:

1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

2. School of Computer Science and Technology, China University of Petroleum, Qingdao, China

3. Digital Research Institute, ENN Group, Langfang, China

4. Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China

5. School of Artificial Intelligence, Anhui University, Anhui, China

6. Department of Computer and Information Science, University of Macau, Macau, China

7. Institute of State Key Laboratory of Digital Household Appliances, Qingdao, China

8. Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/10414252/10058174.pdf?arnumber=10058174

Reference39 articles.

1. The future of digital health with federated learning

2. Exploiting Sentence Embedding for Medical Question Answering

3. Intelligent Question Answering System Based on Knowledge Graph

4. Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks

5. SPBERT: an Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs

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