In-context query reformulation for failing SPARQL queries
| Autor: | James A. Hendler, Taylor Cassidy, Amar Viswanathan, James Michaelis, Geeth de Mel |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Decision support system
Information retrieval Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 020206 networking & telecommunications 02 engineering and technology computer.file_format Knowledge graph Knowledge extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) SPARQL RDF computer RDF query language computer.programming_language |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2266590 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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