Contextual semantic embeddings for ontology subsumption prediction
Contextual semantic embeddings for ontology subsumption prediction
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DOI：
10.1007/s11280-023-01169-9
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Jiaoyan Chen;Yuan He;E. Jiménez-Ruiz;Hang Dong;Ian Horrocks
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作者：
Jiaoyan Chen;Yuan He;E. Jiménez-Ruiz;Hang Dong;Ian Horrocks
自动构建和精选本体是知识工程和人工智能领域的一项重要但具有挑战性的任务。通过上下文语义嵌入等机器学习技术进行预测是一个很有前途的方向，但相关的研究还处于初步阶段，尤其是针对Web本体语言(OWL)中的可表达本体。本文针对OWL本体类，提出了一种新的包含预测方法BERTSubs。它利用预先训练的语言模型BERT来计算类的上下文嵌入，其中提出了定制模板来结合类上下文(例如，相邻类)和逻辑存在约束。BERTSubs能够预测多种类型的包含项，包括来自同一本体或另一本体的命名类，以及来自同一本体的存在限制。对三个不同包含任务的五个真实世界本体的广泛评估表明了模板的有效性，并且BERTSubs可以显著优于使用(文字感知的)知识图嵌入、非上下文词嵌入和最先进的OWL本体嵌入的基线。
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.