Impact of ontologies and data structures on retrieval augmented generation systems in manufacturing simulation software
Abstract
Manufacturing simulation software enables engineers to model, analyse, and optimise production systems using both structured and unstructured data. These platforms support virtual representations of manufacturing processes, equipment, and workflows, relying on extensive libraries of simulation components to capture domain-specific knowledge. In such environments, effective data management and retrieval are essential for informed decision making. This thesis investigates how ontologies and data structures affect the performance of Large Language Models (LLMs) in data from manufacturing simulation software, focusing on Retrieval Augmented Generation (RAG) systems. To investigate, three case studies were conducted. The first evaluated RAG systems using FAISS vector stores, comparing local and cloud based embedding models on proprietary eCatalog data, and showed that schema rich, structured data and advanced prompt engineering significantly improved retrieval accuracy, with cloud-based models outperforming local alternatives, especially for complex, implicit queries. The second explored knowledge graph-based retrieval using Neo4j, assessing various retriever architectures, including hybrid approaches that combine vector, full-text, and graph traversal methods. It was found that knowledge graphs paired with hybrid retrieval strategies excel at handling context rich and relational queries. The third investigated LLM-powered agents interacting with a relational SQLite3 database…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.