Linked Data Queriesby a Trialogical Learning Approach

Querying Linked (Open) Data (LOD) by directly using SPARQL could be a painful task for most potential users of semantic data. Several approaches have been proposed to help users in query formulation. They succeed in hiding the underlying complexity but exploit only the monological - individual - approach. Information seeking and retrieval is not merely an individual effort, but it inherently involves various collaborative activities. For this reason, our proposal is to facilitate the exploitation of LODs by wrapping the querying and visualization tool in a social platform environment. In this way, we enable the dialogical approach. Moreover, since the users can collaboratively create datasets and visualizations, and reuse them also out of the social platform, we reach the trialogical learning. In this paper, we present our design approach, our tool, and related tests.