Learning Fuzzy SPARQL User Preferences In this paper, we propose an adaptive fuzzy user profiling method for SPARQL: an RDF query language 1. This work extends the study 2 where we proposed a manner by which we enrich SPARQL with fuzzy user preferences expression. According to our approach, users issue generic fuzzy quantified queries that are further refined based on hisher past interactions with the system. Unlike 2, we avoid prompting the user for manual expression of hisher preferences. Online preference learning approaches are by definition adaptive to changes over time of the user preferences which make them more attractive than their static counterpart. In order to achieve online learning, we resort to stochastic search and propose to integrate two different types of user feedback, namely rankbased and scorebased. The efficiency of this approach was validated by some experimental results.

A preliminary investigation into SPARQL query complexity and federation in Bio2RDF When users query a SPARQL endpoint, they normally face an empty text box in which they have to write the desired queries. This obstructs the process of obtaining the data they want, since users rarely have any assistance in querying a (possibly huge) RDF database. In this paper we report a deep analysis of the server log files that record the queries that users send to the SPARQL endpoints, focusing in the Bio2RDF cluster. This log analysis reveals the large number of repeated queries that users submit, and how they pursue a trial and error process by adding and removing operations from the submitted queries to obtain the desired results. We also show how users try to connect to other RDF datasets in the Linked Open Data cloud. Our results offer insight into the interaction between users and a schemalight RDF dataset, and secondly, suggest improvements to SPARQL server optimizations in terms of optimization and results caching.