From Ontologies to Transformers: Natural Language Understanding for Scientific and Operational Texts

Abstract

Natural language understanding for scientific and operational texts has evolved from rigid symbolic pipelines to flexible neural architectures. Ontologies, terminologies, and rule based engines have enabled structured reasoning over domain specific concepts, while deep learning has delivered strong performance on language modeling and sequence classification tasks. Scientific articles, clinical notes, maintenance logs, and operational reports combine formal technical language with local abbreviations and narrative fragments. This article introduces a hybrid framework that connects ontology driven representation with transformer based models for robust natural language understanding across scientific and operational text streams. The framework is evaluated on classification, retrieval, and decision support tasks using realistic domain scenarios, and it demonstrates how structured knowledge and neural representations can be combined to support traceable, data driven decisions.