Abstract
OWL (Web Ontology Language) ontologies which are able to formally represent complex knowledge and support semantic reasoning have been widely adopted across various domains such as healthcare and bioinformatics. Recently, ontology embeddings have gained wide attention due to its potential to infer plausible new knowledge and approximate complex reasoning. However, existing methods face notable limitations: geometric model-based embeddings typically overlook valuable textual information, resulting in suboptimal performance, while the approaches that incorporate text, which are often based on language models, fail to preserve the logical structure. In this work, we propose a new ontology embedding method OnT, which tunes a Pretrained Language Model (PLM) via geometric modeling in a hyperbolic space for effectively incorporating textual labels and simultaneously preserving class hierarchies and other logical relationships of Description Logic EL. Extensive experiments on four real-world ontologies show that OnT consistently outperforms the baselines including the state-of-the-art across both tasks of prediction and inference of axioms. OnT also demonstrates strong potential in real-world applications, indicated by its robust transfer learning abilities and effectiveness in real cases of constructing a new ontology from SNOMED CT. Data and code are available at this https URL.
Abstract (translated)
OWL（Web本体语言）本体能够正式表示复杂知识并支持语义推理，在医疗保健和生物信息学等多个领域得到了广泛应用。最近，由于其潜在的推断合理的新知识和近似复杂推理的能力，本体嵌入技术引起了广泛关注。然而，现有的方法面临显著限制：基于几何模型的嵌入通常忽略了有价值的文本信息，导致性能不佳；而那些结合文本的方法往往基于语言模型，却无法保留逻辑结构。在本文中，我们提出了一种新的本体嵌入方法OnT，该方法通过在双曲空间中的几何建模调优预训练的语言模型（PLM），从而有效整合文本标签的同时保持描述逻辑EL的类层次和其他逻辑关系。四项真实世界本体上的广泛实验表明，在预测和公理推理两项任务中，OnT始终优于包括最先进在内的基线方法。此外，OnT还展示了在现实应用中的巨大潜力，这体现在其强大的迁移学习能力和从SNOMED CT构建新本体的实际案例的有效性上。数据和代码可在该URL获取：[此链接]（请将[此链接]替换为实际的URL）。
URL
https://arxiv.org/abs/2507.14334
