VARAT: Variable Annotation Tool for Documents on Manufacturing Processes
Building physical models is essential for realizing digital twins in the manufacturing industry. This task, however, is labor-intensive and requires a deep understanding of target processes and extensive knowledge from various literature sources. Although this extensive workload can be mitigated by...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Journal of Chemical Engineering of Japan |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00219592.2025.2454461 |
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| author | Shota Kato Manabu Kano |
| author_facet | Shota Kato Manabu Kano |
| author_sort | Shota Kato |
| collection | DOAJ |
| description | Building physical models is essential for realizing digital twins in the manufacturing industry. This task, however, is labor-intensive and requires a deep understanding of target processes and extensive knowledge from various literature sources. Although this extensive workload can be mitigated by automated extraction of information from the literature, developing such methods necessitates domain-specific datasets lacking in chemical engineering. To address this problem, we developed an algorithm for extracting variable symbols from documents and a variable annotation tool, VARAT, based on this algorithm. Our proposed algorithm, tested on 47 papers on physical models of five manufacturing processes, achieved a recall of 97% and a precision of 96%. VARAT was subsequently employed to create a dataset containing 1,988 variable symbols from the 47 papers. This tool reduced the annotation time per paper by more than half. VARAT is expected to accelerate the development of datasets vital for chemical engineering information extraction and ultimately facilitate the development of physical models. |
| format | Article |
| id | doaj-art-f58364902c5e44c1bee96879c41b76b5 |
| institution | Kabale University |
| issn | 0021-9592 1881-1299 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Chemical Engineering of Japan |
| spelling | doaj-art-f58364902c5e44c1bee96879c41b76b52025-02-05T16:40:52ZengTaylor & Francis GroupJournal of Chemical Engineering of Japan0021-95921881-12992025-12-0158110.1080/00219592.2025.2454461VARAT: Variable Annotation Tool for Documents on Manufacturing ProcessesShota Kato0Manabu Kano1Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, JapanGraduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, JapanBuilding physical models is essential for realizing digital twins in the manufacturing industry. This task, however, is labor-intensive and requires a deep understanding of target processes and extensive knowledge from various literature sources. Although this extensive workload can be mitigated by automated extraction of information from the literature, developing such methods necessitates domain-specific datasets lacking in chemical engineering. To address this problem, we developed an algorithm for extracting variable symbols from documents and a variable annotation tool, VARAT, based on this algorithm. Our proposed algorithm, tested on 47 papers on physical models of five manufacturing processes, achieved a recall of 97% and a precision of 96%. VARAT was subsequently employed to create a dataset containing 1,988 variable symbols from the 47 papers. This tool reduced the annotation time per paper by more than half. VARAT is expected to accelerate the development of datasets vital for chemical engineering information extraction and ultimately facilitate the development of physical models.https://www.tandfonline.com/doi/10.1080/00219592.2025.2454461AnnotationInformation extractionDocument understandingMathematical expressionsVariable extraction |
| spellingShingle | Shota Kato Manabu Kano VARAT: Variable Annotation Tool for Documents on Manufacturing Processes Journal of Chemical Engineering of Japan Annotation Information extraction Document understanding Mathematical expressions Variable extraction |
| title | VARAT: Variable Annotation Tool for Documents on Manufacturing Processes |
| title_full | VARAT: Variable Annotation Tool for Documents on Manufacturing Processes |
| title_fullStr | VARAT: Variable Annotation Tool for Documents on Manufacturing Processes |
| title_full_unstemmed | VARAT: Variable Annotation Tool for Documents on Manufacturing Processes |
| title_short | VARAT: Variable Annotation Tool for Documents on Manufacturing Processes |
| title_sort | varat variable annotation tool for documents on manufacturing processes |
| topic | Annotation Information extraction Document understanding Mathematical expressions Variable extraction |
| url | https://www.tandfonline.com/doi/10.1080/00219592.2025.2454461 |
| work_keys_str_mv | AT shotakato varatvariableannotationtoolfordocumentsonmanufacturingprocesses AT manabukano varatvariableannotationtoolfordocumentsonmanufacturingprocesses |