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As knowledge grows faster than ever, transforming unstructured text into structured, machine-understandable knowledge remains a central challenge in AI. This challenge explores the next frontier of Ontology Learning (OL)—moving beyond isolated subtasks toward fully integrated, intelligent systems. Building upon LLMs4OL tasks paradigms, participants will tackle three visionary scenarios: building ontologies end-to-end from raw text, continuously extending existing knowledge structures, and learning taxonomies that generalize across domains. Together, these tasks reflect real-world demands where knowledge is dynamic, interconnected, and domain-spanning. Powered by the OntoLearner framework (https://ontolearner.readthedocs.io/), the challenge provides a unified environment for reproducible benchmarking, modular pipelines, and scalable evaluation. It invites researchers to push the limits of large language models in constructing, evolving, and transferring structured knowledge.
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