Learning Fuzzy SPARQL User Preferences
Olfa Slama, Anis Yazidi · 2019
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 his/her past interactions with the system. Unlike [2], we avoid prompting the user for manual expression of his/her 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 counter-part. In order to achieve online learning, we resort to stochastic search and propose to integrate two different types of user feedback, namely rank-based and score-based. The efficiency of this approach was validated by some experimental results.