arXiv AI
When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
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arXiv AI
Jul 3
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
arXiv:2607. 01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices.
arXiv Computation and Language
Sep 10
OntologyAligner: Ontology-Aligned Retrieval and Hierarchy-Guided Large Language Model Reranking for Biomedical Ontology Normalization
arXiv:2609.10055v1 Announce Type: cross Abstract: Biomedical ontology normalization maps free-text expressions to standardized concepts, enabling consistent integration and analysis of biomedical dat...
Hugging Face Trending Papers
Sep 9
OntologyAligner: Ontology-Aligned Retrieval and Hierarchy-Guided Large Language Model Reranking for Biomedical Ontology Normalization
Biomedical ontology normalization maps free-text expressions to standardized concepts, enabling consistent integration and analysis of biomedical data. This task remains challenging because lexical va...
arXiv AI
Sep 10
OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?
arXiv:2609.08174v1 Announce Type: new Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs a...
arXiv AI
Aug 28
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.
arXiv AI
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Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes
arXiv:2605. 28965v2 Announce Type: replace Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data.