Abstract
In this paper, we describe scalable highly expressive reasoner (SHER), a breakthrough technology that provides semantic querying of large relational datasets using OWL ontologies. SHER relies on a unique algorithm based on ontology summarization and combines a traditional in-memory description logic reasoner with a database backed RDF Store to scale reasoning to very large Aboxes. In our latest experiments, SHER is able to do sound and complete conjunctive query answering up to 7 million triples in seconds, and scales to datasets with 60 million triples, responding to queries in minutes. We describe the SHER system architecture, discuss the underlying components and their functionality, and briefly highlight two concrete use-cases of scalable OWL reasoning based on SHER in the Health Care and Life Science space.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
