Exploiting RDFS and OWL for Integrating Heterogeneous, Large-Scale, Linked Data Corpora

262 Pages·2011·2.65 MB·English

by  Aidan Hogan

Exploiting RDFS and OWL for Integrating
Heterogeneous, Large-Scale, Linked Data Corpora
Aidan Hogan
Supervisor: Dr. Axel Polleres
Internal Examiner: Prof. Stefan Decker
External Examiner: Prof. James A. Hendler
Dissertation submitted in pursuance of the degree of Doctor of Philosophy
Digital Enterprise Research Institute, Galway
National University of Ireland, Galway / Ollscoil na hE´ireann, Gaillimh
April 11, 2011
Copyright (cid:13)c Aidan Hogan, 2011
The research presented herein was supported by an IRCSET Postgraduate Scholarship and by Science Foundation
Ireland under Grant No. SFI/02/CE1/I131 (Lion) and Grant No. SFI/08/CE/I1380 (Lion-2).
“If you have an apple and I have an apple and we exchange these apples then you
and I will still each have one apple. But if you have an idea and I have an idea and
we exchange these ideas, then each of us will have two ideas.”
—George Bernard Shaw

Acknowledgements
First, thanks to the taxpayers for the pizza and (much needed) cigarettes;
...thanks to friends and family;
...thanks to the various students and staff of DERI;
...thanks to the URQ folk;
...thanks to people with whom I have worked closely, including Alex, Antoine, Jeff, Luigi and Piero;
...thanks to people with whom I have worked very closely, particularly Andreas and Ju¨rgen;
...thanks to John and Stefan for the guidance;
...thanks to Jim for the patience and valuable time;
...and finally, a big thanks to Axel for everything.
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Abstract
The Web contains a vast amount of information on an abundance of topics, much of which is encoded
as structured data indexed by local databases. However, these databases are rarely interconnected and
information reuse across sites is limited. Semantic Web standards offer a possible solution in the form
of an agreed-upon data model and set of syntaxes, as well as metalanguages for publishing schema-level
information, offering a highly-interoperable means of publishing and interlinking structured data on the
Web. Thanks to the Linked Data community, an unprecedented lode of such data has now been published
ontheWeb—byindividuals,academia,communities,corporationsandgovernmentalorganisationsalike—on
a medley of often overlapping topics.
Thisnewpublishingparadigmhasopeneduparangeofnewandinterestingresearchtopicswithrespectto
howthisemergent“WebofData”canbeharnessedandexploitedbyconsumers. Indeed,althoughSemantic
Web standards theoretically enable a high level of interoperability, heterogeneity still poses a significant
obstacle when consuming this information: in particular, publishers may describe analogous information
using different terminology, or may assign different identifiers to the same referents. Consumers must also
overcometheclassicalchallengesofprocessingWebdatasourcedfrommultitudinousandunvettedproviders:
primarily, scalability and noise.
Inthisthesis,welookattacklingtheproblemofheterogeneitywithrespecttoconsuminglarge-scalecor-
poraofLinkedDataaggregatedfrommillionsofsourcesontheWeb. Assuch,wedesignbespokealgorithms—
inparticular,basedontheSemanticWebstandardsandtraditionalInformationRetrievaltechniques—which
leverage the declarative schemata (a.k.a. terminology) and various statistical measures to help smooth out
the heterogeneity of such Linked Data corpora in a scalable and robust manner. All of our methods are
distributed over a cluster of commodity hardware, which typically allows for enhancing performance and/or
scale by adding more machines.
WefirstpresentadistributedcrawlerforcollectingagenericLinkedDatacorpusfrommillionsofsources;
we perform an open crawl to acquire an evaluation corpus for our thesis, consisting of 1.118 billion facts of
information collected from 3.985 million individual documents hosted by 783 different domains. Thereafter,
we present our distributed algorithm for performing a links-based analysis of the data-sources (documents)
comprising the corpus, where the resultant ranks are used in subsequent chapters as an indication of the
importance and trustworthiness of the information they contain. Next, we look at custom techniques for
performing rule-based materialisation, leveraging RDFS and OWL semantics to infer new information, of-
ten using mappings—provided by the publishers themselves—to translate between different terminologies.
Thereafter,wepresentaformalframeworkforincorporatingmetainformation—relatingtotrust,provenance
and data-quality—intothis inferencingprocedure; in particular, we deriveand track rankingvaluesfor facts
based on the sources they originate from, later using them to repair identified noise (logical inconsistencies)
in the data. Finally, we look at two methods for consolidating coreferent identifiers in the corpus, and we
present an approach for discovering and repairing incorrect coreference through analysis of inconsistencies.
Throughout the thesis, we empirically demonstrate our methods against our real-world Linked Data corpus,
and on a cluster of nine machines.
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Declaration
I declare that this thesis is composed by myself, that the work contained herein is my own except where
explicitly stated otherwise in the text, and that this work has not been submitted for any other degree or
professional qualification except as specified.
Aidan Hogan
April 11, 2011
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