OnDBTuning is a relational database (semi-automatic) tuning ontology. Ontologies are artifacts that represent specific domain knowledge and can be used to infer knowledge. However, in general, most applications include only a formal and static description of concepts. Moreover, as database tuning involves many rules-of-thumb and black-box algorithms, it becomes challenging to describe these inference procedures. This research work first presents the OnDBTuning ontology solution focusing on the inference of tuning actions. Next, it proposes an implementation of the OnDBtuning rules using SPARQL Inferencing Notation (SPIN). Finally, it shows a practical evaluation of our solution concerning index and materialized views recommendations.