  The emergence of tools based on artificial intelligence has also led to the
need of producing explanations which are understandable by a human being. In
most approaches, the system is considered a black box, making it difficult to
generate appropriate explanations. In this work, though, we consider a setting
where models are transparent: probabilistic logic programming (PLP), a paradigm
that combines logic programming for knowledge representation and probability to
model uncertainty. However, given a query, the usual notion of explanation is
associated with a set of choices, one for each random variable of the model.
Unfortunately, such a set does not explain why the query is true and, in fact,
it may contain choices that are actually irrelevant for the considered query.
To improve this situation, we present in this paper an approach to explaining
explanations which is based on defining a new query-driven inference mechanism
for PLP where proofs are labeled with "choice expressions", a compact and easy
to manipulate representation for sets of choices. The combination of proof
trees and choice expressions allows us to produce comprehensible query
justifications with a causal structure.
