Reasoning Techniques for the Web of Data

341 Pages·2014·2.678 MB·English

by  Hogan, A.

Reasoning Techniques for
the Web of Data
Aidan Hogan
Digital Enterprise Research Institute,
National University of Ireland, Galway
Department of Computer Science,
Universidad de Chile
IOS Press
Preface
This book is based on a thesis I submitted to the National University
ofIreland,GalwayinpursuanceofaDoctorateofPhilosophyinFebru-
ary2011. (Ipassed!) Thethesiswasoriginallytitled“ExploitingRDFS
andOWLforIntegratingHeterogeneous,Large-Scale,LinkedDataCor-
pora”. MysupervisorwasAxelPolleres,myinternalexaminerwasSte-
fan Decker and my external examiner was Jim Hendler. In this version
I’ve ironed out a few kinks and made some corrections. I have also
abbreviated some parts to maintain a reasonable length.
Returningtoworkonathesisagainafterthreeyearscanmakeone
philosophical about the PhD process. I don’t wish to get philosophical
hereexcepttosaythateveninthedarkestdaysofmyPhD,Iwasdriven
bytheideathattheWebhasnotcomeclosetorealisingitsfullpotential
yet, and that perhaps I could influence, in some small but meaningful
way, what the Web will look like in ten, twenty or fifty years time.
(What will it look like?)
For better or worse, I bet my early twenties on investigating meth-
ods by which deductive reasoning can be applied over lots of diverse
Web data without making an enormous mess, and this thesis was the
result. Automated deductive reasoning techniques can help machines
better understand and process the content of the Web, leading to new
applications. But needless to say, it was and still is an ambitious goal.
Three years the wiser, the work presented herein has yet to revolu-
tionise the Web. But I learnt a lot from the PhD process. And I would
like to think that, in its own unique way, this work demonstrates that
the original vision of the Semantic Web, with “altruistic” machines au-
tomatically acting upon their “deep understanding” of Web content, is
not science fiction. Machines can certainly do useful things by reason-
ing over Web data. The only question is to what extent such reasoning
will be needed by the future Web of ten, twenty or fifty years time.

Acknowledgements
First and foremost, I am indebted to my supervisor, Axel Polleres, for
his careful and considered guidance. I am also grateful to my internal
examiner, Stefan Decker, and my external examiner, Jim Hendler.
Though I am down as the only author of this work, there are many
people that have influenced its contents. In particular, I would like
to acknowledge the contributions of Andreas Harth, Jürgen Umbrich,
Piero Bonatti, Luigi Sauro and Antoine Zimmermann to the work pre-
sented herein. And Axel of course!
This work was supported by a number of funds, including an IRC-
SET Postgraduate Scholarship, support by Science Foundation Ireland
(SFI)underGrantNo. SFI/08/CE/I1380(Lion-2)andalsounderGrant
No. SFI/12/RC/2289 (INSIGHT), and support by the Millennium Nu-
cleus Center for Semantic Web Research under Grant NC120004.

Abstract
The Web contains a vast amount of information on an abundance of
topics. Muchofthisinformationisencodedasstructureddataindexed
by various databases. However, these databases are rarely intercon-
nected and information reuse across sites is limited.
Semantic Web standards offer a possible solution in the form of an
agreed-upondatamodelandsetofsyntaxes,aswellasmetalanguages
for publishingschema-level information. Intheory, these standardsof-
fer a highly-interoperable means of publishing and interlinking struc-
tured data on the Web. In practice, the Linked Data community have
overseen the publication of a vast quality of such data on the Web—by
individuals, academia, communities, corporations and governmental
organisations alike—on a medley of often overlapping topics.
This new publishing paradigm has opened up a range of new and
interesting research topics with respect to how this emergent “Web of
Data”canbeharnessedandexploitedbyconsumers. Indeed,although
Semantic Web standards theoretically enable a high level of interoper-
ability, heterogeneity still poses a significant obstacle when consuming
this information: in particular, publishers may describe analogous in-
formation using different terminology, or may assign different identi-
fierstothesamereferents. Consumersmustalsoovercometheclassical
challengesofprocessingWebdatasourcedfrommultitudinousandun-
vetted providers: primarily, scalability and noise.
Inthisthesis,wetackletheproblemofheterogeneitywithrespectto
consumingcorporaofLinkedDataaggregatedfrommillionsofsources
on the Web. As such, we design bespoke algorithms—in particular,
based on the Semantic Web standards and traditional Information Re-
trieval techniques—that leverage the declarative schemata (a.k.a. ter-
minology)andvariousstatisticalmeasurestohelpsmoothoutthehet-
erogeneityofsuchLinkedDatacorporainascalableandrobustmanner.
x Abstract
All of our methods are distributed over a cluster of commodity hard-
ware, such that typically allows for improving performance and/or in-
creasing scale by adding more machines.
WefirstpresentadistributedcrawlerforcollectingagenericLinked
Datacorpusfrommillionsofsources; weperformanopencrawltoac-
quire an evaluation corpus for our thesis containing 1.118 billion facts
collected from 3.985 million individual documents hosted by 783 dif-
ferentdomains. Wethenpresentourdistributedalgorithmforperform-
ing 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 informa-
tion they contain. Next we look at custom techniques for performing
rule-based materialisation, leveraging RDFSand OWL semantics toin-
fernewinformation,oftenusingmappings—providedbythepublishers
themselves—to translate between different terminologies. Thereafter
we present a formal framework for incorporating metainformation—
relating to trust, provenance and data-quality—into this inferencing
procedure; in particular, we derive and track ranking values for 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.
Throughoutthiswork,weempiricallydemonstrateourmethodsby
applyingthemoverourreal-worldLinkedDatacorpus,runningexper-
iments over a cluster of nine commodity machines.
Contents
I Prologue 1
1 Introduction 3
1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . 7
1.1.1 Incomplete Agreement on Assertional Identifiers 8
1.1.2 Use of Analogous Terminologies . . . . . . . . . . 9
1.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Contribution and Thesis Structure . . . . . . . . . . . . . 13
1.4 Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Background 19
2.1 The World Wide Web . . . . . . . . . . . . . . . . . . . . . 19
2.2 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1 Resource Description Framework . . . . . . . . . 23
2.2.2 RDF Schema . . . . . . . . . . . . . . . . . . . . . . 30
2.2.3 Web Ontology Language . . . . . . . . . . . . . . . 32
2.3 RDF Web Publishing and Linked Data . . . . . . . . . . . 37
2.4 RDF Search Engines . . . . . . . . . . . . . . . . . . . . . . 39
3 Notation and Core Concepts 43
3.1 RDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 Turtle Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Linked Data Principles and Provenance . . . . . . . . . . 46
3.4 Atoms and Rules . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5 Terminological Data: RDFS/OWL . . . . . . . . . . . . . . 49
3.6 Distribution Framework . . . . . . . . . . . . . . . . . . . . 51
4 Crawling, Corpus and Ranking 53
4.1 Crawler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.1.1 Breadth-first Crawling . . . . . . . . . . . . . . . . 56
4.1.2 Incorporating Politeness . . . . . . . . . . . . . . . 56
4.1.3 On-disk Queue . . . . . . . . . . . . . . . . . . . . . 57
xii CONTENTS
4.1.4 Multi-threading . . . . . . . . . . . . . . . . . . . . 58
4.1.5 Crawling RDF/XML . . . . . . . . . . . . . . . . . . 59
4.1.6 Distributed Approach . . . . . . . . . . . . . . . . . 62
4.1.7 Related Work . . . . . . . . . . . . . . . . . . . . . . 64
4.1.8 Critical Discussion and Future Directions. . . . . 66
4.2 Evaluation Corpus . . . . . . . . . . . . . . . . . . . . . . . 67
4.2.1 Crawl Statistics . . . . . . . . . . . . . . . . . . . . 67
4.2.2 Corpus Statistics . . . . . . . . . . . . . . . . . . . . 69
4.2.3 Related Work . . . . . . . . . . . . . . . . . . . . . . 72
4.2.4 Critical Discussion and Future Directions. . . . . 76
4.3 Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.3.1 Rationale and High-level Approach . . . . . . . . 77
4.3.2 Creating and Ranking the Source Graph . . . . . 79
4.3.3 Distributed Ranking Implementation . . . . . . . 80
4.3.4 Ranking Evaluation and Results . . . . . . . . . . 81
4.3.5 Related Work . . . . . . . . . . . . . . . . . . . . . . 82
4.3.6 Critical Discussion and Future Directions. . . . . 83
II Core 85
5 Web Reasoning 87
5.1 Linked Data Reasoning: Overview . . . . . . . . . . . . . 90
5.1.1 Incomplete Reasoning: Rationale . . . . . . . . . 91
5.1.2 Rule-based Reasoning . . . . . . . . . . . . . . . . 93
5.1.3 Forward Chaining . . . . . . . . . . . . . . . . . . . 96
5.1.4 OWL 2 RL/RDF Scalability . . . . . . . . . . . . . 97
5.2 Distinguishing Terminological Data . . . . . . . . . . . . . 99
5.2.1 Implementing T-split Inferencing . . . . . . . . . . 111
5.3 Optimising the Assertional Program . . . . . . . . . . . . 114
5.3.1 Merging Equivalent T-ground Rules . . . . . . . . 114
5.3.2 Rule Index . . . . . . . . . . . . . . . . . . . . . . . 117
5.3.3 Rule Saturation . . . . . . . . . . . . . . . . . . . . 120
5.3.4 Preliminary Performance Evaluation . . . . . . . 121
5.4 Towards Linked Data Reasoning . . . . . . . . . . . . . . . 124
5.4.1 “A-linear” OWL 2 RL/RDF . . . . . . . . . . . . . . 125
5.4.2 Authoritative Reasoning . . . . . . . . . . . . . . . 129
5.4.3 Distributed Reasoning . . . . . . . . . . . . . . . . 134
5.4.4 Linked Data Reasoning Evaluation . . . . . . . . . 136
CONTENTS xiii
5.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5.5.1 Scalable/Distributed Reasoning . . . . . . . . . . 149
5.5.2 Web Reasoning. . . . . . . . . . . . . . . . . . . . . 151
5.6 Critical Discussion and Future Directions . . . . . . . . . 153
6 Annotated Reasoning 157
6.1 Generalised Annotated Programs . . . . . . . . . . . . . . 158
6.2 Use-case Annotations . . . . . . . . . . . . . . . . . . . . . 160
6.2.1 Blacklisting . . . . . . . . . . . . . . . . . . . . . . . 161
6.2.2 Authoritative Analysis . . . . . . . . . . . . . . . . 161
6.2.3 Triple Ranks . . . . . . . . . . . . . . . . . . . . . . 161
6.3 Formal Annotation Framework . . . . . . . . . . . . . . . 162
6.3.1 Annotation Domains . . . . . . . . . . . . . . . . . 163
6.3.2 (Specialised) Annotated Programs . . . . . . . . . 163
6.3.3 Least Fixpoint and Decidability . . . . . . . . . . . 164
6.3.4 Seeding Annotations . . . . . . . . . . . . . . . . . 169
6.3.5 T-split Annotated Programs . . . . . . . . . . . . . 170
6.3.6 Annotated Reasoning Tasks . . . . . . . . . . . . . 172
6.3.7 Constraints . . . . . . . . . . . . . . . . . . . . . . . 184
6.4 Annotated Linked Data Reasoning . . . . . . . . . . . . . 187
6.4.1 Ranking Triples: Implementation/Evaluation . . 188
6.4.2 Reasoning: Implementation/Evaluation . . . . . 189
6.4.3 Repair: Implementation/Evaluation . . . . . . . . 194
6.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 207
6.5.1 Annotated Reasoning . . . . . . . . . . . . . . . . . 207
6.5.2 Inconsistency Repair . . . . . . . . . . . . . . . . . 209
6.6 Critical Discussion and Future Directions . . . . . . . . . 209
7 Consolidation 213
7.1 OWL Equality Semantics . . . . . . . . . . . . . . . . . . . 218
7.2 Corpus: Naming Across Sources . . . . . . . . . . . . . . . 220
7.3 Base-line Consolidation . . . . . . . . . . . . . . . . . . . . 223
7.3.1 High-level approach . . . . . . . . . . . . . . . . . 223
7.3.2 Distributed approach . . . . . . . . . . . . . . . . . 224
7.3.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . 225
7.4 Extended Reasoning Consolidation . . . . . . . . . . . . . 228
7.4.1 High-level approach . . . . . . . . . . . . . . . . . 228
7.4.2 Distributed approach . . . . . . . . . . . . . . . . . 244
7.4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . 245
7.5 Statistical Concurrence Analysis (Synopsis) . . . . . . . . 251