Integrating knowledge graphs and large language models for querying in an industrial environmentHočevar, Domen (Avtor) Robnik Šikonja, Marko (Mentor) Kenda, Klemen (Komentor) knowledge graphlarge language modelChatGPTretrieval-augmented generationIndustry 4.0We describe an application that converts Industry 4.0 Administration Shell representations into a knowledge graph and stores the generated knowledge graph and vector embeddings of graph nodes in a GraphDB repository and a vector database, respectively. The application supports user queries in natu- ral language on the stored knowledge graph using a large language model and retrieval-augmented generation (RAG). Two different approaches for retrieval are used: subgraph retrieval and graph query generation. The application also has a front-end, through which users can perform all the aforementioned operations. The application was tested on mock data that represents a simple factory consisting of multiple different types of machines. Its performance was shown on an array of expected queries, showcasing its efficiency and specific strengths of the subgraph retrieval and graph query generation approaches.20242024-09-03 16:00:00Diplomsko delo/naloga160705sl