  Identifying the secondary structure of an RNA is crucial for understanding
its diverse regulatory functions. This paper focuses on how to enhance target
identification in a Boltzmann ensemble of structures via chemical probing data.
We employ an information-theoretic approach to solve the problem, via
considering a variant of the R\'{e}nyi-Ulam game. Our framework is centered
around the ensemble tree, a hierarchical bi-partition of the input ensemble,
that is constructed by recursively querying about whether or not a base pair of
maximum information entropy is contained in the target. These queries are
answered via relating local with global probing data, employing the modularity
in RNA secondary structures. We present that leaves of the tree are comprised
of sub-samples exhibiting a distinguished structure with high probability. In
particular, for a Boltzmann ensemble incorporating probing data, which is well
established in the literature, the probability of our framework correctly
identifying the target in the leaf is greater than $90\%$.
