In-context query reformulation for failing SPARQL queries
Amar Viswanathan, James Michaelis, Taylor Cassidy, Geeth R. de Mel, James Hendler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Knowledge bases for decision support systems are growing increasingly complex, through continued advances in data ingest and management approaches. However, humans do not possess the cognitive capabilities to retain a bird’s-eyeview of such knowledge bases, and may end up issuing unsatisfiable queries to such systems. This work focuses on the implementation of a query reformulation approach for graph-based knowledge bases, specifically designed to support the Resource Description Framework (RDF). The reformulation approach presented is instance-and schema-aware. Thus, in contrast to relaxation techniques found in the state-of-the-art, the presented approach produces in-context query reformulation.