How is Your Knowledge Graph Used: Content-Centric Analysis of SPARQL Query Logs

Knowledge graphs (KGs) are used to integrate and persist information useful to organisations, communities, or the general public. It is essential to understand how KGs are used so as to evaluate the strengths and shortcomings of semantic web standards, data modelling choices formalised in ontologies, deployment settings of triple stores etc. One source of information on the usage of the KGs is the query logs, but making sense of hundreds of thousands of log entries is not trivial. Previous works that studied available logs from public SPARQL endpoints mainly focused on the general syntactic properties of the queries disregarding the semantics and their intent. We introduce a novel, content-centric, approach that we call query log summarisation, in which we group the queries that can be derived from some common pattern. The type of patterns considered in this work is query templates, i.e. common blueprints from which multiple queries can be generated by the replacement of parameters with constants. Moreover, we present an algorithm able to summarise a query log as a list of templates whose time and space complexity is linear with respect to the size of the input (number and dimension of queries). We experimented with the algorithm on the query logs of the Linked SPARQL Queries dataset showing promising results.