An FCA Framework for Knowledge Discovery in SPARQL Query Answers

Formal concept analysis (FCA) is used for knowledge discovery within data. In FCA, concept lattices are very good tools for classification and organization of data. Hence, they can also be used to visualize the answers of a SPARQL query instead of the usual answer formats such as: RDF/XML, JSON, CSV, and HTML. Consequently, in this work, we apply FCA to reveal and visualize hidden relations within SPARQL query answers by means of concept lattices.