Ontology-driven software engineering using LLMs for knowledge graphs in engineering biology

Abstract

Large language models have transformed software engineering practices. However, generated artefacts are not always developer-friendly and may partially meet complex requirements. As the need to standardise, integrate, and develop tools in engineering biology increases, novel approaches are needed to create and maintain intuitive software sustainably. Here, we present an ontology-driven approach using large language models to create user-facing software libraries for knowledge graphs. We introduce an ontology-to-language framework to systematically map domain terms and graph structures. We then demonstrate this approach by creating an ontology for the latest Synthetic Biology Open Language standard and generating the sbol-script software library, which can be used within browsers or to develop applications with native web support. This ontology-driven software engineering approach and these resources are essential for the community and to facilitate the development of sustainable software projects. The SBOL3 Ontology and the sbol-script library are available from https://github.com/SynBioDex/sbol-owl3 and https://github.com/SynBioDex/sbol-script.