一种自动化方法,用于特定领域的知识图表生成――图表测量和表征.
Connor O'Ryan1, Kevin D Hayes1, Francis G VanGessel2
1Center for Engineering Concepts Development, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, United States.
Journal of chemical information and modeling
|January 28, 2025
概括
本研究介绍了一种自然语言处理 (NLP) 方法,可以从科学文本中创建知识图,从而从广泛的化学文献中更好地提取信息,并识别合成中的语言趋势.
科学领域:
- 计算化学计算化学
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 科学出版物的指数增长需要自动化方法来提取信息.
- 现有的知识图提取方法往往是特定于领域的和有限的.
- 弥合人工智能进步和科学文献分析之间的差距至关重要.
研究的目的:
- 开发一种新的自然语言处理 (NLP) 方法,从技术文档中提取知识图.
- 从合成化学专利创建一个语义结构网络 (SSN).
- 描述由此产生的知识图,用于语言和趋势分析.
主要方法:
- 开发了一个自然语言处理 (NLP) 模型用于知识图表提取.
- 将该模型应用于大约10万份全长合成化学专利.
- 使用网络图案结构,分类性和自身向量的中心性进行了图形表征.
主要成果:
- 成功地从化学专利中提取了一个语义结构网络 (SSN).
- 在化学反应话语中确定了语言模式,包括常见的溶剂和化合物命名.
- 在较大的文本体中观察到权力法趋势,表明可扩展性.
结论:
- 开发的NLP方法为专业领域的知识图表提取提供了强大的方法.
- 知识图的定量表征有助于理解科学话语和验证大型数据集.
- 这项工作促进了对化学合成文献的更深入的了解,并使跨领域的知识图比较成为可能.
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