The construction of domain knowledge graphs is a key strategy for achieving industrial intelligence and promoting collaborative data intelligence within industries. Relational databases in enterprise information systems contain vast amounts of business data, which serve as a crucial source of industry knowledge. However, traditional rule-based ontology extraction methods often perform poorly when applied to enterprise databases with diverse design paradigms and expanding scales. Moreover, ontologies generated from different databases may vary in structure, which poses new challenges to achieving cross-enterprise data collaboration. As a result, designing an efficient and high-quality automated ontology extraction method remains a major challenge. To this end, we propose a domain knowledge extraction approach for relational databases (KE4RDB). First, we design a hierarchical three-layer ontology model that not only reduces the cost of mapping relational data to ontologies but also improves the efficiency of knowledge extraction. Based on this model, we develop a method for extracting knowledge from relational databases and integrating it into unified domain ontologies. Finally, we conducted experiments on three domain-specific databases, demonstrating that the KE4RDB performs excellently in domain knowledge extraction and ontology generation tasks.