Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques such as contextual semantic embedding is a promising direction, but the relevant research is still preliminary especially for expressive ontologies in Web Ontology Language (OWL). In this paper, we present a new subsumption prediction method named BERTSubs for classes of OWL ontology. It exploits the pre-trained language model BERT to compute contextual embeddings of a class, where customized templates are proposed to incorporate the class context (e.g., neighbouring classes) and the logical existential restriction. BERTSubs is able to predict multiple kinds of subsumers including named classes from the same ontology or another ontology, and existential restrictions from the same ontology. Extensive evaluation on five real-world ontologies for three different subsumption tasks has shown the effectiveness of the templates and that BERTSubs can dramatically outperform the baselines that use (literal-aware) knowledge graph embeddings, non-contextual word embeddings and the state-of-the-art OWL ontology embeddings.

翻译：自动化本体构建与维护是知识工程与人工智能领域一项重要而富有挑战的任务。基于上下文语义嵌入等机器学习技术的预测方法展现出广阔前景，但相关研究仍处于初步阶段，尤其针对Web本体语言（OWL）中具有表达力的本体。本文提出了一种名为BERTSubs的新型子类关系预测方法，适用于OWL本体中的类。该方法利用预训练语言模型BERT计算类的上下文嵌入，通过定制化模板融入类上下文（如邻接类）及逻辑存在性约束。BERTSubs能够预测多种类型的父概念，包括同一本体或其他本体中的命名类，以及同一本体中的存在性约束。在五个真实世界本体上针对三项不同子类关系预测任务开展的广泛评估表明，该模板具有有效性，且BERTSubs能显著优于基于（字面感知的）知识图谱嵌入、非上下文词嵌入及当前最先进的OWL本体嵌入的基线方法。