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

Towards Inferring Queries from Simple and Partial Provenance Examples