Table Of ContentUnsupervised and Semi-Supervised Learning
Series Editor: M. Emre Celebi
Deepak P
Anna Jurek-Loughrey Editors
Linking and Mining
Heterogeneous and
Multi-view Data
Unsupervised and Semi-Supervised Learning
Springer’s Unsupervised and Semi-Supervised Learning book series covers the
latest theoretical and practical developments in unsupervised and semi-supervised
learning.Titles–includingmonographs,contributedworks,professionalbooks,and
textbooks–tacklevariousissuessurroundingtheproliferationofmassiveamounts
of unlabeled data in many application domains and how unsupervised learning
algorithms can automatically discover interesting and useful patterns in such
data. The books discuss how these algorithms have found numerous applications
including pattern recognition, market basket analysis, web mining, social network
analysis, information retrieval, recommender systems, market research, intrusion
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which can make use of both labeled and unlabeled data and can be useful in
applicationdomainswhereunlabeleddataisabundant,yetitispossibletoobtaina
smallamountoflabeleddata.
Moreinformationaboutthisseriesathttp://www.springer.com/series/15892
Deepak P (cid:129) Anna Jurek-Loughrey
Editors
Linking and Mining
Heterogeneous and
Multi-view Data
123
Editors
DeepakP AnnaJurek-Loughrey
Queen’sUniversityBelfast Queen’sUniversityBelfast
NorthernIreland,UK NorthernIreland,UK
ISSN2522-848X ISSN2522-8498 (electronic)
UnsupervisedandSemi-SupervisedLearning
ISBN978-3-030-01871-9 ISBN978-3-030-01872-6 (eBook)
https://doi.org/10.1007/978-3-030-01872-6
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Preface
Withdatabeingrecordedatneverseenbeforescaleswithinanincreasinglycomplex
world,computingsystemsroutinelyneedtodealwithalargevarietyofdatatypes
fromapluralityofdatasources.Moreoftenthannot,thesameentitymaymanifest
across different data sources differently. To be able to arrive at a well-rounded
view across all data sources to fuel data-driven applications, such different entity
representations may need to be linked, and such linked information be mined
while being mindful of the differences between the different views. The goal of
this volume is to offer a snapshot of the advances in this emerging area of data
science and big data systems. The intended audience includes researchers and
practitioners who work on applications and methodologies related to linking and
miningheterogeneousandmulti-viewdata.
The book covers a number of chapters on methods and algorithms—including
both novel methods and surveys—and some that are more application-oriented
and targeted towards specific domains. To keep the narrative interesting and to
avoid monotony for the reader, chapters on methods and applications have been
interleaved.
The first four chapters provide reviews of some of the foundational streams of
research in the area. The first chapter is an overview of state-of-the-art methods
for multi-view data completion, a task of much importance since each entity may
notberepresentedfullyacrossalldifferentviews.Thesecondchaptersurveysthe
fieldofmulti-viewdataclustering,themostexploredareaofunsupervisedlearning
within the realm of multi-view data. We then turn our attention to linking, with
the third chapter taking stock of unsupervised and semi-supervised methods to
linkingdatarecordsfromacrossdatasources.Recordlinkageisacomputationally
expensive task, and blocking methods often come in handy to rein in the search
space.Unsupervisedandsemi-supervisedblockingtechniquesformthefocusofthe
fourthchapter.
Thefifthchapteristhefirstofapplicationarticlesinthevolume,anditsurveysa
richfieldofapplicationofmulti-viewdataanalytics,thatoftransportationsystems
thatencompassawidevarietyofsensorscapturingdifferentkindsoftransportation
data.Socialmediadiscussionsoftenmanifestwithintricatestructurealongwiththe
v
vi Preface
textual content, and identifying discourse acts within such data forms the topic of
thesixthchapter.Theseventhchapterlooksatimbalanceddatasetswithaneyeon
improvingper-classclassificationaccuracy.Theyproposenovelmethodsthatmodel
the co-operation between different data views in order to improve classification
accuracy. The eighth chapter is focused on an enterprise information retrieval
scenario, where a fast and simple model for linking entities from domain-specific
knowledgegraphstoIR-stylequeriesisproposed.
Chapter 9 takes us back to multi-view clustering and focuses on surveying
methodsthatbuilduponnon-negativematrixfactorizationandmanifoldlearningfor
thetask.Thetenthchapterconsidershowheterogeneousdatasourcesareleveraged
indatasciencemethodsforfakenewsdetection,anemergingareaofhighinterest
from a societal perspective. Chapter 11 is an expanded version of a conference
paperthatappearedinAISTATS2018andaddressesthetaskofmulti-viewmetric
learning. Social media analytics, in addition to leveraging conventional types of
data, is characterized by abundant usage of structure. Community detection is
amongthefundamentaltaskswithinsocialmediaanalytics;thelastchapterinthis
volumeconsidersevaluationofcommunitydetectionmethodsforsocialmedia.
We hope that this volume, focused on both linking heterogeneous data sources
andmethodsforanalyticsoverlinkeddatasources,willdemonstratethesignificant
progress that has occurred in this field in recent years. We also hope that the
developmentsreportedinthisvolumewillmotivatefurtherresearchinthisexciting
field.
Belfast,UK DeepakP
Belfast,UK AnnaJurek-Loughrey
Contents
1 Multi-ViewDataCompletion.............................................. 1
SahelyBhadra
2 Multi-ViewClustering ..................................................... 27
DeepakPandAnnaJurek-Loughrey
3 Semi-supervisedandUnsupervisedApproaches toRecord
PairsClassificationinMulti-SourceDataLinkage ..................... 55
AnnaJurek-LoughreyandDeepakP
4 A Review of Unsupervised and Semi-supervised Blocking
MethodsforRecordLinkage.............................................. 79
KevinO’Hare,AnnaJurek-Loughrey,andCassiodeCampos
5 TrafficSensingandAssessinginDigitalTransportationSystems..... 107
HanaRabbouch,FouedSaâdaoui,andRafaaMraihi
6 HowDidtheDiscussionGo:DiscourseActClassification
inSocialMediaConversations ............................................ 137
SubhabrataDutta,TanmoyChakraborty,andDipankarDas
7 Learning from Imbalanced Datasets with Cross-View
Cooperation-BasedEnsembleMethods.................................. 161
CécileCapponiandSokolKoço
8 EntityLinkinginEnterpriseSearch:CombiningTextual
andStructuralInformation ............................................... 183
SumitBhatia
9 Clustering Multi-View Data Using Non-negative Matrix
Factorization and Manifold Learning for Effective
Understanding:ASurveyPaper.......................................... 201
KhanhLuongandRichiNayak
vii
viii Contents
10 LeveragingHeterogeneousDataforFakeNewsDetection............. 229
K.Anoop,ManjaryP.Gangan,DeepakP,andV.L.Lajish
11 GeneralFrameworkforMulti-ViewMetricLearning ................. 265
RiikkaHuusari,HachemKadri,andCécileCapponi
12 OntheEvaluationofCommunityDetectionAlgorithms
onHeterogeneousSocialMediaData .................................... 295
AntonelaTommaselandDanielaGodoy
Index............................................................................... 335
Chapter 1
Multi-View Data Completion
SahelyBhadra
Abstract Multi-view learning has been explored in various applications such as
bioinformatics,naturallanguageprocessingandmultimediaanalysis.Oftenmulti-
view learning methods commonly assume that full feature matrices or kernel
matricesforallviewsareavailable.However,inpartialdataanalytics,itiscommon
thatinformationfromsomesourcesisnotavailableormissingforsomedata-points.
Such lack of information can be categorized into two types. (1) Incomplete view:
information of a data-point is partially missing in some views. (2) Missing view:
information of a data-point is entirely missing in some views, but information for
thatdata-pointisfullyavailableinotherviews(nopartiallymissingdata-pointina
view).
Althoughmulti-viewlearninginthepresenceofmissingdatahasdrawnagreat
amountofattentionintherecentpastandtherearequitealotofresearchpaperson
multi-viewdatacompletion,butthereisnocomprehensiveintroductionandreview
of current approaches on multi-view data completion. We address this gap in this
chapterthroughdescribingthemulti-viewdatacompletionmethods.
Inthischapter,wewillmainlydiscussexistingmethodstodealwithmissingview
problem. Wedescribeasimpletaxonomy of thecurrentapproaches. And foreach
category, representative as well as newly proposed models are presented. We also
attempttoidentifypromisingavenuesandpointoutsomespecificchallengeswhich
canhopefullypromotefurtherresearchinthisrapidlydevelopingfield.
1.1 Introduction
Multi-View Data Multi-view learning is an emerging field to deal with data
collectedfrommultiplesourcesor“views”toutilizethecomplementaryinformation
inthem.Incaseofmulti-viewlearning,thescientificdataarecollectedfromdiverse
S.Bhadra((cid:2))
IndianInstituteofTechnology,Palakkad,India
e-mail:[email protected];https://sites.google.com/iitpkd.ac.in/sahelybhadra/home
©SpringerNatureSwitzerlandAG2019 1
DeepakP,A.Jurek-Loughrey(eds.),LinkingandMiningHeterogeneous
andMulti-viewData,UnsupervisedandSemi-SupervisedLearning,
https://doi.org/10.1007/978-3-030-01872-6_1
