Background

The Semantic Web aims to enrich the current web with structured knowledge and metadata for enhanced interoperability and understanding across systems. Central to this endeavor is Ontology Learning (OL), which automates the extraction of this structured knowledge from unstructured data, which is crucial for building dynamic ontologies foundational to the Semantic Web. The advent of Large Language Models (LLMs) has introduced a promising approach to OL, leveraging their deep linguistic understanding and pattern inference capabilities to automate OL. 

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Our prior work published in the ISWC 2023 research track proceedings titled "LLMs4OL: Large Language Models for Ontology Learning" marked a notable direction toward employing LLMs in OL, demonstrating their potential in automating knowledge acquisition and representation for the Semantic Web. Based on this research, we previously organized the "1st LLMs4OL challenge @ ISWC 2024" as a community development endeavor in response to adapting LLMs for OL. With the 1st LLMs4OL challenge @ ISWC 2024 challenge, we catalyzed community-wide engagement in validating and expanding the use of LLMs in OL. This initiative is poised to advance our comprehension of LLMs’ roles within the Semantic Web, encouraging innovation and collaboration in developing scalable and accurate OL methods. The previous challenge attracted eight teams of participants with novel systems for OL within the Semantic Web community's goals of making the web more intelligent and user-friendly, offering a novel avenue for exploring the intersection of LLMs and OL. 

Now, the 2nd LLMs4OL Challenge@ISWC-2025 is a continuous community development endeavor as a part of the 24th ISWC. With the 2nd LLMs4OL challenge, we aim to catalyze further community-wide engagement in validating and expanding the use of LLMs in OL.  This challenge aims to align with the Semantic Web community's goals of making the web more intelligent and user-friendly, offering a novel avenue for exploring the intersection of LLMs and OL. Participation in this challenge will contribute to evolving the Semantic Web, enabling more sophisticated services that utilize structured knowledge effectively.