Hofer, M. ; Frey, J. ; Rahm, E.

Towards self-configuring Knowledge Graph Construction Pipelines using LLMs - A Case Study with RML

Proceedings of the 5th International Workshop on Knowledge Graph Construction co-located with 21th Extended Semantic Web Conference (ESWC 2024)

2024 / 05


Futher information: https://ceur-ws.org/Vol-3718/paper6.pdf


This paper explores using large language models (LLMs) to generate RDF mapping language (RML) files in the RDF turtle format as a key step towards self-configuring RDF knowledge graph construction pipelines. Our case study involves mapping a subset of the Internet Movie Database (IMDB) in JSON format given a target Movie ontology (selection of DBpedia Ontology OWL statements). We define and compute several scores to assess both the generated mapping files and the resulting graph using a manually created reference. Our findings demonstrate the promising potential of the state-of-the-art commercial LLMs in a zero-shot scenario.