Scalable authoritative owl reasoning for the web
Hogan, Aidan Harth, Andreas Polleres, Axel
Hogan, Aidan
Harth, Andreas
Polleres, Axel
Publication Date
2009-01-01
Type
Article
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Hogan, Aidan; Harth, Andreas; Polleres, Axel (2009). Scalable authoritative owl reasoning for the web. International Journal on Semantic Web and Information Systems 5 (2), 49-90
Abstract
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 stheirces" which counter-acts an observed behavitheir which we 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 stheirces and present scale-up experiments on a dataset in the order of a billion statements collected from the Web. [Article copies are available for purchase from InfoSci-on-Demand.com]
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IGI Global
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Attribution-NonCommercial-NoDerivs 3.0 Ireland