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Scalable Authoritative OWL Reasoning for the Web

Scalable Authoritative OWL Reasoning for the Web

Aidan Hogan, Andreas Harth, Axel Polleres
Copyright: © 2010 |Pages: 44
ISBN13: 9781605669823|ISBN10: 1605669822|EISBN13: 9781605669830
DOI: 10.4018/978-1-60566-982-3.ch116
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MLA

Hogan, Aidan, et al. "Scalable Authoritative OWL Reasoning for the Web." Web Technologies: Concepts, Methodologies, Tools, and Applications, edited by Arthur Tatnall, IGI Global, 2010, pp. 2206-2249. https://doi.org/10.4018/978-1-60566-982-3.ch116

APA

Hogan, A., Harth, A., & Polleres, A. (2010). Scalable Authoritative OWL Reasoning for the Web. In A. Tatnall (Ed.), Web Technologies: Concepts, Methodologies, Tools, and Applications (pp. 2206-2249). IGI Global. https://doi.org/10.4018/978-1-60566-982-3.ch116

Chicago

Hogan, Aidan, Andreas Harth, and Axel Polleres. "Scalable Authoritative OWL Reasoning for the Web." In Web Technologies: Concepts, Methodologies, Tools, and Applications, edited by Arthur Tatnall, 2206-2249. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-982-3.ch116

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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 rulebased 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.

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