Semi-automatic staging area for high-quality structured data extraction from scientific literature

Abstract

ABSTRACTWe 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.

Keywords

• Materials informatics
• superconductors
• machine learning
• database
• TDM