Real-time, Best-effort Query Processing of Semantic Web data

2016 Fiscal Year Final Research Report

• Project/Area Number: 15K15994
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Multimedia database
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: Lynden Steven 国立研究開発法人産業技術総合研究所, 人工知能研究センター, 研究員 (30528279)
• Project Period (FY): 2015-04-01 – 2017-03-31
• Keywords: Semantic Web / Linked Open Data / Query Processing

Outline of Final Research Achievements

The project achieved the following contributions to Semantic Web query processing technology. Techniques were developed for optimising user criteria during live-exploration based distributed RDF query processing, which have resulted in a novel approach to answering queries within fixed time constraints. The approach enables criteria such as freshness, diversity or coverage to be increased during a fixed-time interval query over Web documents containing RDF data. Techniques were developed for automatic linking of structured data on the Web with existing Linked Open Data knowledge bases, and machine learning approaches were applied to the problem of predicting the behaviour of Semantic Web data providers (SPARQL endpoints) in order to support the optimisation of distributed query plans where no prior information (metadata or statistics) is available about individual endpoints.

Free Research Field

Computer Science