Scalable Authoritative OWL Reasoning for the Web


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 counteracts an observed behavitheir which we term " ontology hijacking " : new ontologies published BLOCKINon BLOCKINthe BLOCKINWeb BLOCKINre-defining BLOCKINthe BLOCKINsemantics 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, BLOCKINon-disk BLOCKINsorts BLOCKINand BLOCKINfile-scans. BLOCKINThe BLOCKINauthors 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.

DOI: 10.4018/jswis.2009040103

Extracted Key Phrases

Showing 1-6 of 6 references

International Journal of Information Technology and Web Engineering

  • F Jairo, Rubens N De Souza, Jonice Melo, Oliveira
  • 2010 structural/47026

  • Www Improving Collaborations in the Neuroscientist Community

  • Www community/72952?camid=4v1a

  • Www


Citations per Year

100 Citations

Semantic Scholar estimates that this publication has received between 69 and 150 citations based on the available data.

See our FAQ for additional information.