There is a strong demand for automation of Database Management Systems (DBMS) tasks, as those related to self-management and self-tuning activities. However, whenever automatic decisions are made, there is also a lack of clearness about the considered decisions and actions. This thesis proposes a framework to support the DBA (and possibly other database users) on choices concerning tuning activities. This research work includes the proposal of an ontology for (autonomous or not) database tuning that enables a formal approach for decisions and inferences. The goals are to offer transparency and confidence on the available tuning alternatives with respect to the possible DBMS scenarios through a concrete justification about the decisions that are made. Moreover, new tuning practices may be obtained automatically as soon as new rules, concepts and practices are known. Finally, our approach enables an actual combination, at a high level of abstraction, of distinct database tuning strategies.