Scalable Authoritative OWL Reasoning for the Web. Aidan Hogan, A. H. & Polleres, A. In Semantic Services, Interoperability and Web Applications: Emerging Concepts, pages 131-177. IGI Global, June, 2011. Invited re-publication![Scalable Authoritative OWL Reasoning for the Web [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In this chapter, 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 hijackin'': 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. In this republished version, the authors also present extended discussion reflecting upon recent developments in the area of scalable RDFS/OWL reasoning, some of which has drawn inspiration from the original publication (Hogan, et al., 2009).
@incollection{hoga-etal-2011IGI,
Abstract = {In this chapter, 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 hijackin'': 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. In this republished version, the authors also present extended discussion reflecting upon recent developments in the area of scalable RDFS/OWL reasoning, some of which has drawn inspiration from the original publication (Hogan, et al., 2009).},
Author = {Aidan Hogan, Andreas Harth and Axel Polleres},
Booktitle = {Semantic Services, Interoperability and Web Applications: Emerging Concepts},
Editor = {Amit Sheth},
Month = jun,
Note = {Invited re-publication},
Pages = {131-177},
Publisher = {IGI Global},
Title = {Scalable Authoritative OWL Reasoning for the Web},
Type = BC,
Url = {http://www.igi-global.com/bookstore/titledetails.aspx?titleid=47114&detailstype=chapters},
Year = 2011,
Bdsk-Url-1 = {http://www.igi-global.com/bookstore/titledetails.aspx?titleid=47114&detailstype=chapters}}
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