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説明:

We propose a semi-automatic staging area for efficiently building an accurate database of experimental physical properties of superconductors from literature, called SuperCon2, to enrich the existing manually-built superconductor database SuperCon. Here we report our curation interface (SuperCon2 Interface) and a workflow managing the state transitions of each examined record, to validate the dataset of superconductors from PDF documents collected using Grobid-superconductors in a previous work. This curation workflow allows both automatic and manual operations, the former contains ‘anomaly detection’ that scans new data identifying outliers, and a ‘training data collector’ mechanism that collects training data examples based on manual corrections. Such training data collection policy is effective in improving the machine-learning models with a reduced number of examples. For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer. We show that our interface significantly improves the curation quality by boosting precision and recall as compared with the traditional ‘manual correction’. Our semi-automatic approach would provide a solution for achieving a reliable database with text-data mining of scientific documents.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: materials informatics, superconductors, machine learning, database, tdm

刊行年月日: 2023-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) 2286219

研究助成金:

• Research and Development

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2023.2286219

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更新時刻: 2024-07-11 16:30:24 +0900

MDRでの公開時刻: 2024-07-11 16:30:24 +0900