Ontology database: A new method for semantic modeling and an application to brainwave data

Abstract

We propose an automatic method for modeling a relational database that uses SQL triggers and foreign-keys to efficiently answer positive semantic queries about ground instances for a Semantic Web ontology. In contrast with existing knowledge-based approaches, we expend additional space in the database to reduce reasoning at query time. This implementation significantly improves query response time by allowing the system to disregard integrity constraints and other kinds of inferences at run-time. The surprising result of our approach is that load-time appears unaffected, even for medium-sized ontologies. We applied our methodology to the study of brain electroencephalographic (EEG and ERP) data. This case study demonstrates how our methodology can be used to proactively drive the design, storage and exchange of knowledge based on EEG/ERP ontologies. © 2008 Springer-Verlag.