  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.
