Ontologies contain rich knowledge within domain, which can be divided into
two categories, namely extensional knowledge and intensional knowledge.
Extensional knowledge provides information about the concrete instances that
belong to specific concepts in the ontology, while intensional knowledge
details inherent properties, characteristics, and semantic associations among
concepts. However, existing ontology embedding approaches fail to take both
extensional knowledge and intensional knowledge into fine consideration
simultaneously. In this paper, we propose a novel ontology embedding approach
named EIKE (Extensional and Intensional Knowledge Embedding) by representing
ontologies in two spaces, called extensional space and intensional space. EIKE
presents a unified framework for embedding instances, concepts and their
relations in an ontology, applying a geometry-based method to model extensional
knowledge and a pretrained language model to model intensional knowledge, which
can capture both structure information and textual information. Experimental
results show that EIKE significantly outperforms state-of-the-art methods in
three datasets for both triple classification and link prediction, indicating
that EIKE provides a more comprehensive and representative perspective of the
domain.

我们提出了一种名为 EIKE（扩展和内涵知识嵌入）的新颖本体嵌入方法，通过在扩展空间和内涵空间表示本体，应用基于几何的方法来建模扩展知识，并利用预训练语言模型来建模内涵知识，可以捕捉结构信息和文本信息，实验结果表明，EIKE 在三个数据集上的三元分类和链接预测方面显著优于最先进的方法，说明 EIKE 提供了更全面和代表性的领域视角。