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.

A product design ontology for enhancing shape processing in design workflows

Effective and efficient information management and knowledge sharing has become an essential part of more and more professional tasks in the Product Development Process. Among the varius Knowledge Management technologies, ontologies offer new possibilities for representing, handling and retrieving product related knowledge, and for online collaboration. We conceived a Product Design Ontology (PDO) which especially addresses researchers in industrial product design and engineering analysis who need to share shape data and to develop software tools. In particular, we formalised the task-specific information associated to a shape, and the functionality and usage of shape processing methods in specific tasks of the design workflow. The PDO, thanks to the ontologydriven metadata on shapes and tools, may be also useful in industry on the one hand for training and for retrieving shape-related information, on the other hand for supporting benchmarking of processing tools and gathering the knowledge about shape processing workflows.Science Foundation Ireland; Irish Research Council for Science, Engineering & Technolog