Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present Deeponto, a Python package designed for ontology engineering. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to include other essential components including reasoning, verbalisation, normalisation, projection, and more. Building on this module, Deeponto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methodologies, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of Deeponto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).

翻译：将深度学习技术，特别是语言模型（LMs），应用于本体工程已引起广泛关注。然而，PyTorch和TensorFlow等深度学习框架主要面向Python编程开发，而广泛使用的本体API（如OWL API和Jena）则基于Java。为实现这些框架与API的无缝集成，我们推出了DeepOnto——一个专为本体工程设计的Python包。该包包含一个核心本体处理模块，该模块基于广泛认可且可靠的OWL API构建，以更"Python化"的方式封装其基础功能，并扩展其能力以涵盖推理、言语化、规范化、投影等其他关键组件。在此模块基础上，DeepOnto提供了一套工具、资源和算法，通过利用深度学习技术（主要是预训练语言模型）支持本体对齐、补全等多种本体工程任务。本文还通过两个用例展示了DeepOnto的实际效用：英国三星研究院的数字健康指导项目，以及本体对齐评估倡议（OAEI）的生物机器学习赛道。