Graph-based QSS: A Graph-based Approach to Quantifying Semantic Similarity for Automated Linear SQL Grading
Leo Köberlein, Dominik Probst, Richard Lenz · arXiv (Cornell University) · 2024
Determining the Quantified Semantic Similarity (QSS) between database queries is a critical challenge with broad applications, from query log analysis to automated SQL skill assessment. Traditional methods often rely solely on syntactic comparisons or are limited to checking for semantic equivalence. This paper introduces Graph-based QSS, a novel graph-based approach to measure the semantic dissimilarity between SQL queries. Queries are represented as nodes in an implicit graph, while the transitions between nodes are called edits, which are weighted by semantic dissimilarity. We employ shortest path algorithms to identify the lowest-cost edit sequence between two given queries, thereby defining a quantifiable measure of semantic distance. An empirical study of our prototype suggests that our method provides more accurate and comprehensible grading compared to existing techniques. Moreover, the results indicate that our approach comes close to the quality of manual grading, making it a robust tool for diverse database query comparison tasks.