arXiv AI August 28, 2026 By Shivam Mishra, Dhannu Ram Meena, Muneendra Ojha, Krishna Pratap Singh, Kuldeep Singh

pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning

The pro-team at LLMs4OL 2026 presented a system for ontology learning that tackles both the End-to-End Flagship Task (Task A) and the Ontology Extension Reuse Task (Task B). Their approach uses an offline retrieval‑augmented few‑shot prompting pipeline with Qwen2.5‑14B‑Instruct and MiniLM‑L6‑v2 for retrieval, selecting top‑5 examples for Task A and top‑2 for Task B, and applies a left‑truncated context‑windowing strategy to keep task instructions in long prompts. For Task B, generated triples are filtered deterministically by a vocabulary constraint, keeping triples that involve at least one term from the closed vocabulary and removing duplicates of the initial ontology, achieving high scores in Semantic Graph Similarity, Term‑Typing F1, and Taxonomy Discovery F1, though no non‑taxonomic relations were extracted.

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