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Scalable Authoritative OWL Reasoning for the Web (2009)
| Citations: | 45 - 14 self |
BibTeX
@MISC{Hogan09scalableauthoritative,
author = {Aidan Hogan and Andreas Harth and Axel Polleres},
title = { Scalable Authoritative OWL Reasoning for the Web},
year = {2009}
}
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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.
Keyphrases
scalable authoritative owl reasoning pd fragment observed characteristic rule-based framework present scale-up experiment web re-defining rule-based forward-chaining assertional data new ontology undesirable example real-world web source owl fragment lightweight in-memory index terminological data term ontology web data large scale rdf datasets authoritative source observed behaviour scalable authoritative owl reasoner on-disk sort entity resident
