Onishi Takeshi, Kadohira Takuya, Watanabe Ikumu
Toyota Technological Institute at Chicago, Chicago, IL, USA.
Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki, Japan.
Sci Technol Adv Mater. 2018 Sep 19;19(1):649-659. doi: 10.1080/14686996.2018.1500852. eCollection 2018.
In this study, we develop a computer-aided material design system to represent and extract knowledge related to material design from natural language texts. A machine learning model is trained on a text corpus weakly labeled by minimal annotated relationship data (~100 labeled relationships) to extract knowledge from scientific articles. The knowledge is represented by relationships between scientific concepts, such as {}. The extracted relationships are represented as a knowledge graph formatted according to design charts, inspired by the process-structure-property-performance (PSPP) reciprocity. The design chart provides an intuitive effect of processes on properties and prospective processes to achieve the certain desired properties. Our system semantically searches the scientific literature and provides knowledge in the form of a design chart, and we hope it contributes more efficient developments of new materials.
在本研究中，我们开发了一种计算机辅助材料设计系统，用于从自然语言文本中表示和提取与材料设计相关的知识。一个机器学习模型在由最少注释关系数据（约100个标记关系）弱标记的文本语料库上进行训练，以从科学文章中提取知识。这些知识由科学概念之间的关系表示，例如{}。受过程-结构-性能（PSPP）相互关系的启发，提取的关系被表示为根据设计图表格式化的知识图谱。设计图表提供了过程对性能的直观影响以及实现某些期望性能的预期过程。我们的系统对科学文献进行语义搜索，并以设计图表的形式提供知识，我们希望它能为新材料的更高效开发做出贡献。
