基于知识的问答使用图形神经网络和语境语言表示
Mohamed Samir1, Naglaa Fathy2, Walaa Gad2
1Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. mohamed_samir@cis.asu.edu.eg.
Scientific reports
|January 20, 2026
概括
本研究提出了一个新的问答 (QA) 框架,该框架将常识知识图形与高级语言模型相结合. 这种方法显著提高了复杂推理任务的准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 知识表示 知识表示
背景情况:
- 当前的问题答案 (QA) 系统与常识推理作斗争.
- 整合结构化知识与深度学习模型是一个关键的挑战.
研究的目的:
- 开发一个新的质量保证框架,利用常识知识图和深层次的上下文嵌入.
- 通过结合结构化和非结构化数据,提高质量保证系统的推理能力.
主要方法:
- 一个图形神经网络,特别是图形注意网络v2 (GATv2),被用来处理来自ConceptNet的子图.
- 用BERT来生成深层次的语境嵌入问题-答案对.
- 一个融合机制将基于图形的结构化知识与BERT的语言表示相结合.
主要成果:
- 该框架在CommonsenseQA上实现了82.3%的准确性,在OpenBookQA上达到86.21%.
- 在这些常识推理基准上,性能超过了现有的最先进的方法.
- 证明了将知识图与语言模型集成的有效性.
结论:
- 拟议的框架有效地将结构化的常识知识与语言模型理解相结合.
- 这种混合方法显著提升了质量保证能力,特别是在需要细微推理的任务中.
- 未来的工作可以探索进一步整合不同的知识来源.
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