Answering the why-not questions of graph query autocompletion

Abstract

Graph query autocompletion (gQAC) helps users formulate graph queries in a visual environment (a.k.a GUI). It takes a graph query that the user is formulating as input and generates a ranked list of query suggestions. Since it is impossible to accurately predict the user’s target query, the current state-of-the-art of gQAC sometimes fails to produce useful suggestions. In such scenarios, it is natural for the user to ask why are useful suggestions not returned. In this paper, we address the why-not questions of gQAC. Specifically, given an intermediate query q, a target query qt, and a gQAC system X, the why-not questions of gQAC seek for the minimal refinement of the configuration of X, with respect to a penalty model, such that at least one useful suggestion towards qt appears in the returned suggestions. We propose a generic ranking function for existing gQAC systems. We propose a search algorithm for the why-not questions.