Understanding SPARQL Endpoints through Targeted Exploration and Visualization

Abstract

The Resource Description Framework (RDF) has provided a unified way for everyone to publish their data. The SPARQL query language has been developed to facilitate the exploration of this information. The RDF format, however, is addressed mainly to machines and it is not easily comprehended by humans. Due to the value of the information available through the SPARQL endpoints many efforts have been dedicated to facilitate the access and exploration of this information from users with limited knowledge of the Semantic Web. The main challenge of such approaches is the diversity of the information contained the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on knowledge base containing raw data from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.