Annotation and Classification of Graphs of Battery Property in Material Science Literature

Abstract

The task of extracting material property values described in the text of material papers has attracted much attention among materials researchers. However, many property values cannot be extracted by natural language processing alone because property values are often described in graphs rather than in the text in materials papers. In this study, to extract property values from graphs, we constructed a dataset by classifying graphs of property values into classes based on various property conditions such as temperature and time. The dataset was constructed by extracting graph images from a large collection of journal data in the field of materials and utilizing crowdsourcing to annotate the images in a short period of time. In addition, we built several deep learning models and trained and evaluated them on the dataset. As a result, we confirmed the usefulness of our dataset for classifying graphs of property values using deep learning models.