Natural Language Processing-Driven Microscopy Ontology Development

被引：2

作者：

Bayerlein, Bernd ^{[1
]}

Schilling, Markus ^{[1
]}

Curran, Maurice ^{[2
]}

Campbell, Carelyn E. ^{[3
]}

Dima, Alden A. ^{[3
]}

Birkholz, Henk ^{[4
]}

Lau, June W. ^{[3
]}

机构：

[1] Bundesanstalt Materialforschung und prufung BAM, Met High Temp Mat, D-12205 Berlin, Germany

[2] Univ Virginia, Dept Chem Engn, Charlottesville, VA USA

[3] Natl Inst Stand & Technol NIST, Gaithersburg, MD USA

[4] Leibniz Inst Mat Engn, IWT, Bremen, Germany

来源：

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION | 2024年 / 13卷 / 04期

关键词：

Microscopy ontology; Natural language processing; Semantic interoperability; Data integration; Ontology development acceleration; Data discovery enhancement; KNOWLEDGE;

D O I：

10.1007/s40192-024-00378-y

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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 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.

引用

页码：915 / 926

页数：12

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