对生物医学领域的基于知识的引用推理
Pengcheng Li1,2, Kai Zhang3, Xiaozhong Liu3
1School of Economics and Management, Hubei University of Technology, Nanli, Wuhan 430068, No . 28 Rd, Hongshan, District, Hubei China.
Bioinformatics (Oxford, England)
|February 24, 2026
概括
研究人员现在可以理解为什么论文被引用,这要归功于新的AI模型. 该框架解释了生物医学研究中的引用动机,改善了文献导航和知识发现.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 学术传播学术交流
背景情况:
- 引用对于导航科学文献至关重要,但引用的原因往往不清楚.
- 现有的学术搜索工具在排名动机方面缺乏透明度,阻碍了解释,特别是在生物医学等专业领域.
研究的目的:
- 开发一种方法来解释生物医学研究中的引用动机.
- 提高学术搜索结果的透明度和可解释性.
主要方法:
- 开发了一个编码器-解码器框架,整合了精心策划的生物医学知识.
- 该模型以结构化的生物三重体格式生成引用动机的解释.
- 实验涉及从PubMed Central注释超过10,000个与癌症相关的引用关系.
主要成果:
- 拟议的框架优于现有的序列生成模型 (BERT风格,GPT风格).
- 观察到精度,回忆和F1评分的改善,用于引文动机生成.
- 该模型成功地生成了基于已建立的生物医学数据库的生物三胞胎.
结论:
- 这项工作为阐明生物医学文献中引用推理提供了一种新的方法.
- 开发的框架通过提供透明的引用解释来增强学术搜索的实用性.
- 未来的工作可以将这种方法扩展到其他科学领域,并探索更深入的知识整合.
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