← Back to all news
arXiv AI 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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 11

Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)

The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.

By Harshdeep Singh, Yurui Zhu, Giovanni Colavizza, Matteo Romanello
llmsragfine-tuningbenchmarks
More like this →
arXiv AI
Sep 2

Automated Tree Knowledge Graph Construction using Ontology Expansion and Retrieval from Vietnamese History Textbooks

The paper presents an end‑to‑end pipeline for automatically constructing a tree‑structured knowledge graph (KG) from Vietnamese high school history textbooks and evaluating retrieval strategies that exploit the KG’s hierarchical structure. The KG construction uses a three‑phase hybrid relation extraction process, including intra‑batch deduplication, approximate cross‑batch search, and LLM extraction with a centroid filter and dual‑LLM validator, resulting in 750 nodes and 4,341 semantic edges across 41 ontology types. Retrieval evaluation compares three graph traversal strategies—Top‑Down, Horizontal, and Bottom‑Up—on a benchmark of 1,210 Vietnamese queries, finding that the Top‑Down strategy with structural information outperforms a vector baseline by 4.7 percentage points in NDCG@10.

By Ket Doan Nguyen, Minh N. H. Nguyen
llmsragbenchmarks
More like this →