ai.hackcv
论文精选 65arXiv

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs· OntoExtend:需求驱动的大模型本体扩展框架

Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of LLM outputs. This paper introduces OntoExtend, a requirements-driven framework for ontology extension with LLMs. It uses retrieval-augmented generation (RAG) over relevant input ontologies and requirements in the form of competency questions to propose grounded extensions. We evaluate OntoExtend on 39 CQs from two use cases: a public EU-project ontolo

AI 解读论文

利用大模型扩展本体,满足新兴需求

核心方法
提出 OntoExtend 框架,利用 RAG 技术及竞争力问题生成扩展建议
适合谁读
研究者 / 工程师
要解决的问题
现有本体扩展方法缺乏需求驱动,评估有限且容易出错
关键实验
在两个用例的 39 个竞争力问题上进行了评估
主要贡献
实现了需求驱动的本体扩展,并提供了系统性评估方法
意义与局限
提高了本体扩展的准确性和效率,但评估规模有限
领域:cs.AI作者:Anna Sofia Lippolis、Mohammad Javad Saeedizade、Stefan Schmid
相关推荐

本站内容由 LLM 精选聚合,原文版权归 arXiv 所有 · 摘录仅供参考