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Proceedings Paper

In-context query reformulation for failing SPARQL queries
Author(s): Amar Viswanathan; James R. Michaelis; Taylor Cassidy; Geeth de Mel; James Hendler
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Paper 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.

Paper Details

Date Published: 4 May 2017
PDF: 8 pages
Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900M (4 May 2017); doi: 10.1117/12.2266590
Show Author Affiliations
Amar Viswanathan, Rensselaer Polytechnic Institute (United States)
James R. Michaelis, U.S. Army Research Lab. (United States)
Taylor Cassidy, U.S. Army Research Lab. (United States)
Geeth de Mel, IBM Research (United Kingdom)
James Hendler, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 10190:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
Tien Pham; Michael A. Kolodny, Editor(s)

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