Résumé
A well structured collection of the various Quantum Cascade Laser (QCL) design and working properties data provides a platform to analyze and understand the relationships between these properties. This analysis can result in insights into how different design features impact laser performance properties. Most of these QCL properties are captured in scientific text. This poses challenges in generating structured QCL properties data for exploration properties due to the specific nature of this domain. There is therefore a need for efficient methodologies that can be utilized to extract QCL properties from text and generate a semantically enriched and interlinked platform where the properties can be analyzed. There is also the need to maintain provenance and reference information on which these properties are based. Semantic Web technologies such as Ontologies and Knowledge Graphs (KGs) have proven capability in providing interlinked data platforms for knowledge representation in various domains. In this paper, we propose an approach for generating a QCL properties Knowledge Graph (KG) from text. The approach is based on the QCL ontology and a Retrieval Augmented Generation (RAG) enabled information extraction pipeline based on GPT 4-Turbo language model. The properties of interest include: working temperature, laser design type, lasing frequency, laser optical power and the heterostructure. The experimental results demonstrate the feasibility and effectiveness of this approach for efficiently extracting QCL properties from unstructured text and generating a QCL properties Knowledge Graph, which has potential applications in semantic enrichment and analysis of QCL data.