Enhancing Question Answering through Effective Candidate Answer Selection and Mitigation of Incomplete Knowledge Graphs and over-smoothing in Graph Convolutional Networks-Reference-Cited by-同舟云学术

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Enhancing Question Answering through Effective Candidate Answer Selection and Mitigation of Incomplete Knowledge Graphs and over-smoothing in Graph Convolutional Networks

Published:2024-06-30 Issue: Volume:abs/2203.13570 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Li Sirui¹,Wong Kok Wai¹,Zhu Dengya²,Fung Chun Che¹

Affiliation:

1. Murdoch University,Discipline of Information Technology,Western Australia

2. Curtin University,Discipline of Business Information Systems, School of Management and Marketing,Western Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650447.pdf?arnumber=10650447

Reference33 articles.

1. Improving question answering over knowledge graphs using graph summarization;Li

2. Chatkbqa: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models;Luo

3. Unifying large language models and knowledge graphs: A roadmap;Pan

4. Survey of Hallucination in Natural Language Generation

5. Freebase

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