Natural Language Processing-Driven Microscopy Ontology Development

• DOI: 10.1007/s40192-024-00378-y
• Publication Date: 2024-11-11T15:11:21Z
• Authors: Bernd Bayerlein; Markus Schilling; Maurice Curran; Carelyn E. Campbell; Alden A. Dima; Henk Birkholz; June W. Lau
• Keywords: 0303 health sciences; 03 medical and health sciences

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 to Microscopy Microanalysis conference series, we employed two neural network-based algorithms NLP. The goal was semiautomatically create Ontology (MO) that encapsulates interconnects terminology most frequently used by community. MO, characterized its interlinked entities relationships, is designed enhance quality user query results within NexusLIMS. enhancement facilitated through concurrent querying related terms seamless integration logical connections.