The current implementations of ontology classification procedures use the main memory of the computer for loading and processing ontologies, which soon can become one of the main limiting factors for very large ontologies. We describe a secondary memory implementation of a classification procedure for ELH ontologies using an SQL relational database management system. Although secondary memory has much slower characteristics, our preliminary experiments demonstrate that one can obtain a comparable performance to those of existing in-memory reasoners using a number of caching techniques.
['Vincent Delaitre', 'Yevgeny Kazakov']
Classifying ELH ontologies in SQL databases