  The field of query-by-example aims at inferring queries from output examples
given by non-expert users, by finding the underlying logic that binds the
examples. However, for a very small set of examples, it is difficult to
correctly infer such logic. To bridge this gap, previous work suggested
attaching explanations to each output example, modeled as provenance, allowing
users to explain the reason behind their choice of example. In this paper, we
explore the problem of inferring queries from a few output examples and
intuitive explanations. We propose a two step framework: (1) convert the
explanations into (partial) provenance and (2) infer a query that generates the
output examples using a novel algorithm that employs a graph based approach.
This framework is suitable for non-experts as it does not require the
specification of the provenance in its entirety or an understanding of its
structure. We show promising initial experimental results of our approach.
