The Interval Algebra (IA ) and a subset of the Region Connection Calculus ( RCC), namely RCC -8,are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively. Such qualitative information can be formulated as a Qualitative Constraint Network ( QCN ). In this paper, we focus on the minimal labeling problem ( MLP ) and we propose an algorithm to efficiently derive all the feasible base relations of a QCN. Our algorithm considers chordal QCNs and a new form of partial consistency which we define as ◆ G-consistency. Further, the proposed algorithm uses tractable subclasses of relations having a specific patchwork property for which -consistency implies the consistency of the input QCN. Experimentations with QCN s of IA and RCC-8 show the importance and efficiency of this new approach.