arXiv cs.AI· Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu, Lukas Lange, Heiko Paulheim, Daria Stepanova·· 12 小时前AI 评分22
APOLO：面向本体学习的自动提示词优化方法
APOLO: Automatic Prompt Optimization for Ontology Learning
AI 导读
APOLO 将本体学习（OL）转化为对 LLM 模块的显式提示词优化问题，用多智能体系统从专家本体生成文本-本体对作为训练数据。作者提出贪心与自回归两种本体学习器架构，并用基于 DSPy 的贪心进化提示词优化器 GEPA 优化。在生物医学 DOID 与植物 PO 两个本体上，几乎所有模型与模式组合均有提升，自回归学习器增益最大，表明提示词优化可作为微调的轻量替代方案。
正文 · 原文
Abstract:Ontology Learning (OL) from text has advanced with the emergence of Large Language Models (LLMs), but it remains challenging due to the limited availability of annotated training data and the difficulty of adapting LLMs to perform OL effectively. We address this via APOLO - Automatic Prompt Optimization for Ontology Learning, by casting OL as an explicit prompt optimization problem over LLM modules. To obtain training data, we employ a multi-agent system that generates text-ontology pairs from existing expert-curated ontologies. We then propose two ontology learner architectures: a greedy and an autoregressive learner, and optimize both using GEPA, a greedy evolutionary prompt optimizer built on DSPy. Experiments on two ontologies - a biomedical (DOID) and a plant ontology (PO) show consistent improvements after optimization across nearly all model and mode combinations, with autoregressive learners achieving the largest gains. Our results demonstrate that prompt optimization is a viable and lightweight alternative to fine-tuning for OL, and that the autoregressive formulation better captures ontological structure than the greedy approach.
| Comments: | Accepted at the Posters and Demos Track of the International Semantic Web Conference (ISWC 2026) |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.34540 [cs.AI] |
| (or arXiv:2609.34540v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.34540 arXiv-issued DOI via DataCite |
Submission history
From: Heiko Paulheim [view email]
[v1]
Mon, 28 Sep 2026 08:05:12 UTC (1,475 KB)
[v2]
Tue, 20 Oct 2026 07:08:17 UTC (1,415 KB)
来源：arXiv cs.AI · arxiv.org