Luca Foppiano
(National Institute for Materials Science
)
;
Tomoya Mato
(National Institute for Materials Science)
;
Kensei Terashima
(National Institute for Materials Science)
;
Pedro Ortiz Suarez
;
Taku Tou
;
Chikako Sakai
;
Wei-Sheng Wang
(National Institute for Materials Science)
;
Toshiyuki Amagasa
;
Yoshihiko Takano
(National Institute for Materials Science)
;
Masashi Ishii
(National Institute for Materials Science)
Collection
Citation
Description:
(abstract)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.
Rights:
Keyword: materials informatics, superconductors, machine learning, database, tdm
Date published: 2023-12-31
Publisher: Informa UK Limited
Journal:
- Science and Technology of Advanced Materials: Methods (ISSN: 27660400) 2286219
Funding:
- Research and Development
Manuscript type: Publisher's version (Version of record)
MDR DOI:
First published URL: https://doi.org/10.1080/27660400.2023.2286219
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Other identifier(s):
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Updated at: 2024-07-11 16:30:24 +0900
Published on MDR: 2024-07-11 16:30:24 +0900
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Semi-automatic staging area for high-quality structured data extraction from scientific literature.pdf
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