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

16 Mar 2024 (modified: 20 Mar 2024)ESWC 2024 Workshop KGCW SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Knowledge Graph Construction, LLM-KG-Engineering, Automated RML Mapping Generation
TL;DR: Assessing RML mapping generation via LLMs using IMDB data and DBpedia as target Ontology
Abstract: This paper explores using large language models (LLMs) to generate resource 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.
Submission Number: 8