SQuID: Semantic Similarity-Aware Query Intent Discovery

Abstract

Recent expansion of database technology demands a convenient framework for non-expert users to explore datasets. Several approaches exist to assist these non-expert users where they can express their query intent by providing example tuples for their intended query output. However, these approaches treat the structural similarity among the example tuples as the only factor specifying query intent and ignore the richer context present in the data. In this demo, we present SQuID, a system for Semantic similarity-aware Query Intent Discovery. SQuID takes a few example tuples from the user as input, through a simple interface, and consults the database to discover deeper associations among these examples. These data-driven associations reveal the semantic context of the provided examples, allowing SQuID to infer the user's intended query precisely and effectively. SQuID further explains its inference, by displaying the discovered semantic context to the user, who can then provide feedback and tune the result. We demonstrate how SQuID can capture even esoteric and complex semantic contexts, alleviating the need for constructing complex SQL queries, while not requiring the user to have any schema or query language knowledge.