Scalable Authoritative OWL Reasoning for the Web. Hogan, A., Harth, A., & Polleres, A. International Journal on Semantic Web and Information Systems (IJSWIS), 5(2):49–90, IGI Global, 2009. ![Scalable Authoritative OWL Reasoning for the Web [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex In this paper we 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, we compose a rule-based framework for application to web data: we argue our decisions using observations of undesirable examples taken directly from the Web. We further temper our OWL fragment through consideration of ``authoritative sources'' which counter-acts an observed behaviour which we term ``ontology hijacking'': new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. We then present our system for performing rule-based forward-chaining reasoning which we call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, we design our system to scale: our 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. We evaluate our 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.
@article{hoga-etal-2009-ijswis,
Abstract = {In this paper we 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, we compose a rule-based framework for application to web data: we argue our decisions using observations of undesirable examples taken directly from the Web. We further temper our OWL fragment through consideration of ``authoritative sources'' which counter-acts an observed behaviour which we term ``ontology hijacking'': new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. We then present our system for performing rule-based forward-chaining reasoning which we call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, we design our system to scale: our 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. We evaluate our 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.},
Author = {Aidan Hogan and Andreas Harth and Axel Polleres},
Journal = {International Journal on Semantic Web and Information Systems (IJSWIS)},
Number = 2,
Pages = {49--90},
Publisher = {IGI Global},
Title = {Scalable Authoritative OWL Reasoning for the Web},
Type = JOURNAL,
Url = {https://aran.library.nuigalway.ie/bitstream/handle/10379/4891/DERI-TR-2009-04-21.pdf},
Volume = 5,
Year = 2009,
Bdsk-Url-1 = {https://aran.library.nuigalway.ie/bitstream/handle/10379/4891/DERI-TR-2009-04-21.pdf}}
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