Method for Ontology Learning from an RDB: Application to the Domain of Cultural Heritage

For decades, ontologies have defined valuable terminology for describing and representing a knowledge domain, capturing relationships between concepts, and improving knowledge management. Ontologies enable the exchange and sharing of information, extending syntactic and semantic interoperability: such advantages are also very useful in the cultural heritage (CH) field. Nowadays, ontologies are often made manually, although various attempts have been made in the literature for their automatic generation (Ontology Learning). This paper proposes a new way for the semi-automatic building of an ontology from a Relational Database (RDB). Following an accurate review of existing methods, we propose the implementation of a Python library capable of converting an RDB into an OWL ontology and importing its data inside the ontology as concepts and properties instances. We present a case study on actual data from the cultural heritage world coming from the REMIAM project of the High Technology District for Cultural Heritage (Distretto ad Alta Tecnologia per i Beni Culturali - DATABENC). Through interviews with experts in the field, a series of valid questions were identified for the experts’ research work and the interrogation of the knowledge base. The questions were then converted into SQL and SPARQL queries to assess the correctness of the method. The ability of the generated ontology to infer new knowledge on accurate data in the RDB will also be highlighted.