Data in real world applications is often subject to some kind of uncertainty, which can be due to incompleteness, unreliability or inconsistency. This poses a great challenge for ontology-based data access (OBDA) applications, which are expected to provide a meaningful answers to queries, even under uncertain domains. Several extensions of classical OBDA systems has been proposed to address this problem, with probabilistic, possibilistic, and fuzzy OBDA being the most relevant ones. However, these extensions present some limitations with respect to their applicability. Probabilistic OBDA deal only with categorical assertions, possibilistic logic is better suited to make a ranking of axioms, and fuzzy OBDA addresses the problem of modelling vagueness, rather than uncertainty. In this paper we propose Subjective DL-Lite (SDL-Lite), an extension of DL-Lite with Subjective Logic. Subjective DL-Lite allows us to model uncertainty in the data through the application of opinions, which encapsulate our degrees of belief, disbelief and uncertainty for each given assertion. We explore the semantics of Subjective DL-Lite, clarify the main differences with respect to its classical DL-Lite counterpart, and construct a canonical model of the ontology by means of a chase that will serve as the foundation for a future construction of an OBDA system supporting opinions.