一个基于本体学的文本挖掘数据集,用于提取过程结构属性实体
Ali Riza Durmaz1, Akhil Thomas2,3, Lokesh Mishra4
1Fraunhofer Institute for Mechanics of Materials IWM, Freiburg im Breisgau, 79108, Germany. ali.riza.durmaz@iwm.fraunhofer.de.
Scientific data
|October 10, 2024
概括
该MaterioMiner数据集将材料科学文本与用于训练神经符号模型的本体学联系起来. 这种精细的数据集使材料语言模型和知识图表生成的先进研究成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算语言学 计算语言学
- 知识表示 知识表示
背景情况:
- 大型语言模型 (LLM) 在统计语言表现方面表现出色.
- 实体学提供了象征性的知识表示,补充了LLMs.
- 神经符号模型需要集成的文本和本体学数据集用于训练和基准测试.
研究的目的:
- 介绍MaterioMiner数据集及其相关的材料力学本体学.
- 促进神经符号模型在LLMs和本体学的交叉点上的培训和基准测试.
- 实现材料语言模型,自动化本体结构和知识图表生成方面的研究.
主要方法:
- 开发了一个数据集,将文本集与材料力学本体学交织在一起.
- 在四个出版物中注释了2191个实体,有179个不同的类别,确保细致的细节.
- 探索了评级者之间的注释一致性,并为命名实体识别微调了预训练语言模型.
主要成果:
- 建立了MaterioMiner数据集与链接的材料力学本体学.
- 在三位评级者之间展示了高度的注释一致性.
- 展示了使用数据集培训命名实体识别模型的可行性.
结论:
- "MaterioMiner"数据集是推动材料科学中神经象征性AI发展的宝贵资源.
- 它支持材料语言模型的开发和自动化知识提取.
- 方便从科学文献中创建知识图.
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