OWL-DL Based Ontology Inference Engine Assessment for Context-Aware Services

摘要

To acquire hidden and potentially useful information from context data, ubiquitous computing services began taking advantage of the reasoning capabilities inherent in inference engines. However, since a traditional approach to evaluating inference engines' performance levels typically focuses on static information reasoning, specific evaluations of requirements that pertain to the ubiquitous computing environment have been largely neglected. Hence, this paper aims to propose an augmented evaluation framework for inference engines, and then examine how OWL-DL-based inference engines perform by applying them to realistic context-aware services. Six measurement criteria are proposed and measured, including scalability as data set gets large, responsiveness for users' requests, and adaptability to frequent inference requests.