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arXiv 2605.09184cs.AIcs.CLcs.DB

开放本体:基于稳定匹配对齐的工具增强本体工程

Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment

The Tesseract Academy, London, United Kingdom(泰斯拉学院,伦敦,英国)

Fabio Rovai

AI总结:

本文提出Open Ontologies系统,结合LLM驱动构建与形式OWL推理及本体对齐,发现稳定1对1匹配是本体对齐质量关键,且工具增强访问优于纯LLM解析。

摘要:

我们提出了Open Ontologies,一个用Rust实现的开源本体工程系统,整合了LLM驱动构建、形式OWL推理和本体对齐。我们的主要发现是稳定1对1匹配是本体对齐质量的主导因素:在OAEI解剖跟踪中,其F1值为0.832(P=0.963,R=0.733),与最先进的系统相当,并在精度上超过所有其他系统。在五个权重配置的消融研究中,发现当应用稳定匹配时,信号权重无关(F1变化小于0.004),而移除稳定匹配会使F1降至0.728。在会议跟踪中,相同方法实现F1=0.438。在工具增强的本体交互中,我们发现一个LLM读取原始OWL文件(F1=0.323)的表现不如没有文件时的LLM(F1=0.431),而结构化的MCP工具访问实现F1=0.717。这表明工具结构提供了一种LLM无法通过读取原始语法复制的不同的访问模式。该系统以MIT许可证发布为单个二进制文件。

英文摘要:

We present Open Ontologies, an open-source ontology engineering system implemented in Rust that integrates LLM-driven construction with formal OWL reasoning and ontology alignment via the Model Context Protocol. Our primary finding is that stable 1-to-1 matching is the dominant factor in ontology alignment quality: on the OAEI Anatomy track, it achieves F1 = 0.832 (P = 0.963, R = 0.733), competitive with state-of-the-art systems and exceeding all in precision. Ablation across five weight configurations shows that signal weights are irrelevant when stable matching is applied (F1 varies by less than 0.004), while removing stable matching drops F1 to 0.728. On the Conference track, the same method achieves F1 = 0.438. On tool-augmented ontology interaction, we find a surprising result: an LLM reading a raw OWL file (F1 = 0.323) performs worse than the same LLM with no file at all (F1 = 0.431), while structured MCP tool access achieves F1 = 0.717. This demonstrates that tool structure provides a qualitatively different mode of access that the LLM cannot replicate by reading raw syntax. The system ships as a single binary under the MIT licence.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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