arXiv cs.AI· Hui Yang, Ziyu Li, Xuan Liu, Anders Jarfors, He Tan, Jiaoyan Chen·· 2 天前AI 评分21
用嵌入模型与 LLM 精修 CASTON 本体：一种轻量可扩展的工作流
Ontology Refinement with Embeddings and LLMs: A Case Study on CASTON
AI 导读
研究针对铸造领域本体 CASTON，提出结合微调本体嵌入模型与 LLM 的轻量精修工作流：嵌入模型先筛出少量候选公理及替换项，再由 LLM 给出修复建议。在小型专家评估中，采用 CASTON 微调的 OnT 嵌入模型与 Qwen3-8B 的默认配置，6 个高置信度替换全部被两位领域专家接受。实现代码已公开。
正文 · 原文
Abstract:Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare. However, constructing reliable ontologies remains challenging, as class hierarchies, whether manually or automatically constructed, may contain subtle subsumption errors that require careful refinement. We study this problem in CASTON, a casting-domain ontology, and present a lightweight but scalable refinement workflow that combines a fine-tuned ontology embedding model with Large Language Models (LLMs). The embedding model is used to extract a limited number of candidate axioms for refinement together with possible replacement candidates, and an LLM then returns repair suggestions. In a small expert evaluation, the default configuration using a CASTON-fine-tuned OnT embedding model and Qwen3-8B produced reliable high-confidence repairs: all six high-confidence replacements were accepted by both domain experts. The implementation is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.21466 [cs.AI] |
| (or arXiv:2610.21466v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21466 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hui Yang [view email]
[v1]
Thu, 15 Oct 2026 08:10:05 UTC (35 KB)
来源：arXiv cs.AI · arxiv.org