In-context query reformulation for failing SPARQL queries

Amar Viswanathan; James R. Michaelis; Taylor Cassidy; Geeth de Mel; James Hendler

doi:10.1117/12.2266590

4 May 2017 In-context query reformulation for failing SPARQL queries

Amar Viswanathan, James R. Michaelis, Taylor Cassidy, Geeth de Mel, James Hendler

Proceedings Volume 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII; 101900M (2017) https://doi.org/10.1117/12.2266590
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Citation Download Citation

Amar Viswanathan, James R. Michaelis, Taylor Cassidy, Geeth de Mel, and 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); https://doi.org/10.1117/12.2266590

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