Revealing Secrets in SPARQL Session Level

TL;DR

This paper investigates user behaviors during SPARQL query sessions by analyzing real-world logs, revealing patterns that can improve query optimization and caching techniques in Semantic Web applications.

Contribution

It systematically studies session-level user behaviors in SPARQL queries, providing insights for enhancing query prediction and optimization methods.

Findings

Identifies structural and data-driven query change patterns

Provides insights for improving SPARQL caching and auto-completion

Demonstrates potential applications in query suggestion and relaxation

Abstract

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future.