Scalable highly expressive reasoner (SHER)

Julian Dolby, Achille Fokoue, Aditya Kalyanpur, Edith Schonberg, Kavitha Srinivas


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. The SHER system, with the source code, is available for download (free for academic use)

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Type of Paper: System Paper
Keywords: Scalable ontology reasoner; OWL; Summarization; Explanations
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