An approach to RDF(S) query, manipulation and inference on databases

Authors:
Jing Lu;Yong Yu;Kewei Tu;Chenxi Lin;Lei Zhang
Affiliations:
APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Year:
2005

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Abstract

In order to lay a solid foundation for the emerging semantic web, effective and efficient management of large RDF(S) data is in high demand. In this paper we propose an approach to the storage, query, manipulation and inference of large RDF(S) data on top of relational databases. Specifically, RDF(S) inference is done on the database in advance instead of on the fly, so that the query efficiency is maximized. To reduce the cost of inference, two inference modes, the batch mode and the incremental mode, are provided for different scenarios. In both modes, optimized strategies are applied for efficiency purpose. In order to support efficient query and inference on the database, the storage schema is also specially designed. In addition, a powerful RDF(S) query and manipulation language RQML is provided for easy and uniform data access in a declarative way. Finally, we evaluate and report the performance on both query and inference of our approach. Experiments show that our approach achieves encouraging performance in million-scale real data.