4 May 2017 In-context query reformulation for failing SPARQL queries
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Abstract
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.
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Amar Viswanathan, James R. Michaelis, Taylor Cassidy, Geeth de Mel, James Hendler, "In-context query reformulation for failing SPARQL queries", Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900M (4 May 2017); doi: 10.1117/12.2266590; https://doi.org/10.1117/12.2266590
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