Exploratory search on topics through different perspectives with DBpedia

A promising scenario for combining linked data and search is exploratory search. During exploratory search, the search objective is ill-defined and favorable to discovery. A common limit of the existing linked data based exploratory search systems is that they constrain the exploration through single results selection and ranking schemes. The users can not influence the results to reveal specific aspects of knowledge that interest them. The models and algorithms we propose unveil such \textit{knowledge nuances} by allowing the exploration of topics through several perspectives. The users adjust important computation parameters through three operations that help retrieving desired exploration perspectives: specification of interest criteria about the topic explored, controlled randomness injection to reveal unexpected knowledge and choice of the processed knowledge source(s). This paper describes the corresponding models, algorithms and the Discovery Hub implementation. It focuses on the three mentioned operations and presents their evaluations.