LLMs4OL 2024 Datasets: Toward Ontology Learning with Large Language Models

Abstract: Ontology learning (OL) from unstructured data has evolved significantly, with recent advancements integrating large language models (LLMs) to enhance various aspects of the process. The paper introduces the LLMs4OL 2024 datasets, developed to benchmark and advance research in OL using LLMs. The LLMs4OL 2024 dataset as a key component of the LLMs4OL Challenge, targets three primary OL tasks: Term Typing, Taxonomy Discovery, and Non-Taxonomic Relation Extraction. It encompasses seven domains, i.e. lexosemantics and biological functions, offering a comprehensive resource for evaluating LLM-based OL approaches Each task within the dataset is carefully crafted to facilitate both Few-Shot (FS) and Zero-Shot (ZS) evaluation scenarios, allowing for robust assessment of model performance across different knowledge domains to address a critical gap in the field by offering standardized benchmarks for fair comparison for evaluating LLM applications in OL. https://www.tib-op.org/ojs/index.php/ocp/article/view/2480

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: LLMs4OL 2024 Datasets: Toward Ontology Learning with Large Language Models ; volume:4 ; year:2024 ; Open conference proceedings ; 4 (2024)

Contributors, Places and Time

• Creator: Babaei Giglou, Hamed ; D’Souza, Jennifer ; Sadruddin, Sameer ; Auer, Sören

Further information

• DOI: 10.52825/ocp.v4i.2480
• URN: urn:nbn:de:101:1-2411250540384.038273080791
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:24 AM CEST