ISWC 2026, 3rd International Workshop on Large Language Models for Ontology Learning (LLMs4OL), 25-26 October 2026, Bari, Italy
We present a system for constructing ontology graphs from text using openweight large language models. Our approach combines structured prompting, predicate normalization, and structural filtering. Prompt ablation experiments show that explicit taxonomy rules and closed-world constraints improve graph quality. We also introduce a conservative ensemble that retains only triples shared by two prompt configurations. On the official test set, the selected ensemble achieves Graph Similarity scores of 0.3584, 0.4872, and 0.5162 under exact, fuzzy, and semantic matching.
Type:
Conference
City:
Bari
Date:
2026-10-25
Department:
Data Science
Eurecom Ref:
8938
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in ISWC 2026, 3rd International Workshop on Large Language Models for Ontology Learning (LLMs4OL), 25-26 October 2026, Bari, Italy and is available at :
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