Automated Author ProfileSUZUKI, Akira
National Institute for Materials Science0000-0002-8167-0414
SUZUKI, Akira
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 5.9 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
In this study, we developed a new information extraction method using a material dictionary database (MDDB), which parses scientific articles and collects related phrases from various ex-pressions. We used magnetic properties as an illustrative case to analyze the working of the proposed system. Structured terms comprising sub-phrases, tagged words, and their relationships enabled automatic annotation and information extraction. The MDDB was constructed on a pre-built knowledge base that includes information categories and related keywords. These cat-egories can be hierarchically structured and flexibly updated to extract a wide range of information on the associations between magnetic materials and properties along with the measurement systems used, structural analyses performed, and theoretical foundations. Herein, we propose preliminary rule-based phrase collection methods and label pattern extraction for phrases that can easily add new structured terms. We found 1,136 new phrases by label pattern-matching that enabled more related expressions to be retrieved from the text and enhanced the information extraction’s accuracy. Approximately 350 relationships among the material types, properties, and values were extracted from the manually modified annotations of 40 articles on permanent magnets. Our method can be applied to other research domains and can be used by such disciplines to build knowledge bases for any topic in their field.
Authors
- Akira Suzuki
No description available
Authors
- FOPPIANO, Luca ;
- SUZUKI, Akira ;
- DIEB M., Thear ;
- ISHII, Masashi ;
- TANIFUJI, Mikiko