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APOLO system optimizes LLM prompts for ontology learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed APOLO, a novel system for automatic prompt optimization in ontology learning using large language models. APOLO addresses the challenge of limited annotated data by employing a multi-agent system to generate text-ontology pairs from existing expert-curated ontologies. The system proposes two learner architectures, a greedy and an autoregressive learner, which are then optimized using GEPA, a greedy evolutionary prompt optimizer built on DSPy. Experiments on biomedical and plant ontologies demonstrated that prompt optimization significantly improves performance across various model and mode combinations, with the autoregressive formulation showing greater effectiveness in capturing ontological structure compared to the greedy approach. AI

IMPACT This research offers a lightweight alternative to fine-tuning for ontology learning, potentially improving the efficiency and effectiveness of knowledge extraction from text.

RANK_REASON The cluster contains an academic paper detailing a new method for ontology learning using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

APOLO system optimizes LLM prompts for ontology learning

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The cluster contains an academic paper detailing a new method for ontology learning using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu, Lukas Lange, Heiko Paulheim, Daria Stepanova ·

    APOLO: Automatic Prompt Optimization for Ontology Learning

    arXiv:2609.34540v2 Announce Type: replace 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 perfo…

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