Formal mapping rules for ontology learning from relational databases: Handling N-ary relationships and complex constraints

Relational databases (RDBs) remain the backbone of data storage for most applications, yet lack the semantic expressiveness needed for advanced data integration and reasoning in modern environments. Ontologies address this gap by providing formal and machine-readable representations of domain knowledge. Although various approaches exist for transforming RDBs into ontologies, rule-based mapping methods have gained attention due to their systematic nature. However, existing approaches exhibit three critical limitations: (1) inability to handle junction tables with more than two foreign keys, (2) absence of formal validation methods, and (3) inadequate preservation of database integrity constraints. This paper presents a comprehensive solution for transforming RDBs into ontologies based on physical schemas using a schema-driven approach. Our contributions include: (1) a systematic comparison of existing rule-based mapping approaches identifying specific coverage gaps, (2) five novel or corrected mapping rules that extend coverage for complex database patterns including n-ary relationships and self-references, (3) formal representation of all mapping rules using first-order logic enabling transparent understanding and automated validation, and (4) a Z3-based validation pipeline that verifies rule satisfiability and consistency, with concrete experimental results across three domains. We validate our approach through an end-to-end implementation evaluated on database schemas from university management, healthcare, and e-commerce domains, producing OWL ontologies that preserve 15 categories of database constraints compared to 3 for W3C Direct Mapping. Z3 verification confirms logical consistency across all 11 encoded rules, 55 pairwise combinations, and 3 schema-specific instantiations with up to 118 ground facts This work bridges the gap between RDBs and semantic web technologies, providing a formally grounded foundation for schema-driven database-to-ontology transformation.