Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
This addresses the limitation in ontology embedding for domain-specific applications by integrating structural and textual knowledge, though it appears incremental as it builds on existing embedding frameworks.
The paper tackles the problem of embedding ontologies by proposing EIKE, a novel approach that incorporates both extensional and intensional knowledge, resulting in significant outperformance over state-of-the-art methods on three datasets for triple classification and link prediction.
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.