Scalable Authoritative OWL Reasoning for the Web

Scalable Authoritative OWL Reasoning for the Web

Aidan Hogan (National University of Ireland, Ireland), Andreas Harth (National University of Ireland, Ireland) and Axel Polleres (National University of Ireland, Ireland)
Copyright: © 2009 |Pages: 42
DOI: 10.4018/jswis.2009040103
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In this article the authors discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst’s pD* fragment of OWL as a base, the authors compose a rule-based framework for application to web data: they argue their decisions using observations of undesirable examples taken directly from the Web. The authors further temper their OWL fragment through consideration of “authoritative sources” which counter-acts an observed behaviour which they term “ontology hijacking”: new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. They then present their system for performing rule-based forward-chaining reasoning which they call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, they design their system to scale: the system is based upon a separation of terminological data from assertional data and comprises of a lightweight in-memory index, on-disk sorts and file-scans. The authors evaluate their methods on a dataset in the order of a hundred million statements collected from real-world Web sources and present scale-up experiments on a dataset in the order of a billion statements collected from the Web.

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