Natural Language Processing-Driven Microscopy Ontology Development
Bernd Bayerlein; Markus Schilling; Maurice Curran; Carelyn E. Campbell; Alden Dima; Henk Birkholz; June W. Lau
Integrating materials and manufacturing innovation, 2024
doi: 10.1007/s40192-024-00378-y
discovery-gemini-llm-reviewed-20260524
Abstract This manuscript describes the accelerated development of an ontology for microscopy in materials science and engineering, leveraging natural language processing (NLP) techniques. Drawing from a comprehensive corpus comprising over 14 k contributions
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to the Microscopy and Microanalysis conference series, we employed two neural network-based algorithms for NLP. The goal was to semiautomatically create the Microscopy Ontology (MO) that encapsulates and interconnects the terminology most frequently used by the community. The MO, characterized by its interlinked entities and relationships, is designed to enhance the quality of user query results within NexusLIMS. This enhancement is facilitated through the concurrent querying of related terms and the seamless integration of logical connections.
Bernd Bayerlein; Markus Schilling; Maurice Curran; Carelyn E. Campbell; Alden Dima; Henk Birkholz; June W. Lau; Natural Language Processing-Driven Microscopy Ontology Development; Integrating materials and manufacturing innovation; 2024; doi:10.1007/s40192-024-00378-y
Added by matportal-botMay 24, 2026
Repositories
1
Repositorygithub.com
materialdigital/microscopy-ontology
This repository comprises the actual version of the microscopy ontology (MO) developed by the PMDco Core Team in the frame of the PMD project.
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