An Evaluation of Knowledge Base Systems for Large OWL Datasets

Abstract. In this paper, we present an evaluation of four knowledge base systems (KBS) with respect to use in large OWL applications. To our knowledge, no experiment has been done with the scale of data used here. The smallest dataset used consists of 15 OWL files totaling 8MB, while the largest dataset consists of 999 files totaling 583MB. We evaluated two memory-based systems (OWLJessKB and memory-based Sesame) and two systems with persistent storage (database-based Sesame and DLDB-OWL). We describe how we have performed the evaluation and what factors we have considered in it. We show the results of the experiment and discuss the performance of each system. In particular, we have concluded that existing systems need to place a greater emphasis on scalability.

• Type: Conference
• Year: 2004
• Where: SEMWEB
• Authors: Yuanbo Guo, Zhengxiang Pan, Jeff Heflin
• Keywords: Knowledge Base Systems, Large Owl Applications, Memory-based Sesame, SEMWEB 2004