Revealing Secrets in SPARQL Session Level
This work addresses the problem of understanding user behaviors for intention prediction and query optimization in SPARQL-based knowledge graphs, though it is incremental as it builds on existing session-level analysis in other domains.
The paper tackled the lack of systematic research on user behaviors in SPARQL search sessions by conducting a comprehensive investigation over massive real-world query logs, assessing query changes based on structural and data-driven features.
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.