Minerva: a scalable OWL ontology storage and inference system

  • Authors:

  • Jian Zhou;Li Ma;Qiaoling Liu;Lei Zhang;Yong Yu;Yue Pan

  • Affiliations:

  • APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;IBM China Research Lab, Beijing, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;IBM China Research Lab, Beijing, China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;IBM China Research Lab, Beijing, China

  • Venue:
  • ASWC'06 Proceedings of the First Asian conference on The Semantic Web
  • Year:
  • 2006

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Abstract

With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference Furthermore, it customizes the database schema based on inference requirements User queries are answered by directly retrieving materialized results from the back-end database The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.