mlsanityData to 14 Oct 2026

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

arXiv 2608.27101 ↗First posted 27 Aug 2026cs.AIcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs), which can hallucinate domain terms, produce inconsistent formats, and favor hierarchical over associative relations. In the LLMs4OL 2026 Challenge, we address both the End-to-End Flagship Task (Task A) and Ontology Extension Reuse Task (Task B) using an offline retrieval-augmented few-shot prompting pipeline. Our system employs Qwen2.5-14B-Instruct with all-MiniLM-L6-v2 for demonstration retrieval, selecting the top-5 examples for Task A and top-2 for Task B. A left-truncated context-windowing strategy preserves task instructions within long prompts. For Task B, generated triples undergo deterministic vocabulary-constrained filtering, retaining triples when at least one endpoint belongs to the sample's closed term/type vocabulary and removing duplicates of the initial ontology. The approach achieves Semantic Graph Similarity of 0.8692, Term-Typing F1 of 0.9200, and Taxonomy Discovery F1 of 0.8540 on Task B, while Task A achieves 0.7416 Semantic Graph Similarity. However, no non-taxonomic relations are extracted, highlighting limitations of closed, taxonomy-oriented relation vocabularies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 12 references that are in the map. First posted on arXiv 27 Aug 2026.

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6 of 12 references.

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  1. 2025Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)26 Aug

Other references · 5

  1. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 77
  2. 2024Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Fieldcited by 10
  3. 2024LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challengecited by 8
  4. 2023Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphscited by 4
  5. 2025Ontology Learning with LLMs: A Benchmark Study on Axiom Identificationcited by 4

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  1. 2026When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
  2. 2025KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models
  3. 2025Ontology Learning and Knowledge Graph Construction: A Comparison of Approaches and Their Impact on RAG Performance
  4. 2024OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
  5. 2025OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
  6. 2024Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
  7. 2025Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation
  8. 2024A RAG Approach for Generating Competency Questions in Ontology Engineering