Gkoutos, Georgios V

Hoehndorf, Robert

Schofield, Paul N

Slater, Luke

English

Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as most other tools utilizing ontologies, either provide access to ontologies without use of automated reasoning, or limit the number of ontologies for which automated reasoning-based access is provided. We apply the Aber-OWLinfrastructure to provide automated reasoning-based access to all accessible and consistent ontologies in BioPortal (368 ontologies). We perform an extensive performance evaluation to determine query times, both for queries of different complexity as well as for queries that are performed in parallel over the ontologies. We demonstrate that, with the exception of a few ontologies, even complex and parallel queries can now be answered in milliseconds, therefore allowing automated reasoning to be used on a large scale, to run in parallel, and with rapid response times

University of Birmingham Research Portal

Using Aber-OWL for fast and scalable reasoning over BioPortal

http://pure-oai.bham.ac.uk/ws/files/24941059/Slater_et_al_Using_Aber_OWL_2015.pdf

Using Aber-OWL for fast and scalable reasoning over BioPortal

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University of Birmingham Research Portal