Scalable Uncertainty Management: 8th International Conference, SUM 2014, Oxford, UK, September 15-17, 2014. Proceedings

329 Pages·2014·4.248 MB·English

by  Umberto Straccia, Andrea Calì (eds.)

Umberto Straccia
Andrea Calì (Eds.)
0 Scalable Uncertainty
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8

AI Management
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8th International Conference, SUM 2014
Oxford, UK, September 15–17, 2014
Proceedings
123
Lecture Notes in Artificial Intelligence 8720
Subseries of Lecture Notes in Computer Science
LNAISeriesEditors
RandyGoebel
UniversityofAlberta,Edmonton,Canada
YuzuruTanaka
HokkaidoUniversity,Sapporo,Japan
WolfgangWahlster
DFKIandSaarlandUniversity,Saarbrücken,Germany
LNAIFoundingSeriesEditor
JoergSiekmann
DFKIandSaarlandUniversity,Saarbrücken,Germany
Umberto Straccia Andrea Calì (Eds.)
Scalable Uncertainty
Management
8th International Conference, SUM 2014
Oxford, UK, September 15-17, 2014
Proceedings
1 3
VolumeEditors
UmbertoStraccia
IstitutodiScienzaeTecnologiedell’Informazione(ISTI-CNR)
Pisa,Italy
E-mail:[email protected]
AndreaCalì
Birkbeck,UniversityofLondon
DepartmentofComputerScienceandInformationSystems
London,UK
E-mail:[email protected]
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-319-11507-8 e-ISBN978-3-319-11508-5
DOI10.1007/978-3-319-11508-5
SpringerChamHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2014948263
LNCSSublibrary:SL7–ArtificialIntelligence
©SpringerInternationalPublishingSwitzerland2014
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Preface
Information systems are becoming increasingly complex, involving massive
amounts of data coming from different sources. Information is often inconsis-
tent, incomplete, heterogeneous, and pervaded with uncertainty.
The International Conference on Scalable Uncertainty Management (SUM)
conferences series provides an international forum about the management of
uncertain, incomplete, or inconsistent information.
This volume contains the papers presented at the 8th International Confer-
ence on Scalable Uncertainty Management (SUM 2014), which was held at St
Anne’s College, Oxford, UK, from the 15th to the 17th of September, 2014.
The call for papers solicited submissions in two categories: regular research
papersandshortpapers,where the latter reportoninterestingworkinprogress
or provide system descriptions. The call for papers resulted in 47 submissions.
Based on the review reports and discussions, 26 papers were accepted for pub-
lication andpresentationat the conference, among which 20 regular papers and
6 short papers. The conference program also included invited lectures by three
leadingresearchers:AnthonyHunter(DepartmentofComputerScience,Univer-
sityCollegeLondon),JensLehmann(DepartmentofComputerScience,Univer-
sityofLeipzig),andDanOlteanu(DepartmentofComputerScience,University
of Oxford).
Aconferencesuchasthiscanonlysucceedasateameffort. Wewouldliketo
thank:theauthorsofsubmittedpapers,theinvitedspeakers,andtheconference
participants;the members ofthe ProgramCommittee andthe externalreferees;
Alfred Hofmann and Springer for providing assistance and advice in the prepa-
ration of the proceedings; the University of Oxford for providing local facilities;
the creators and maintainers of the conference management system EasyChair.
All of them made the success of SUM 2014 possible.
July 2014 Umberto Straccia
Andrea Cal`ı
Organization
Program Committee
Leila Amgoud IRIT - CNRS, France
Nahla Ben Amor InstitutSuperieurdeGestiondeTunis,Tunisia
Leopoldo Bertossi Carleton University, Canada
Isabelle Bloch ENST - CNRS UMR 5141 LTCI, France
Fernando Bobillo University of Zaragoza, Spain
Loreto Bravo Universidad de Concepcio´n, Chile
Andrea Cal`ı University of London, Birkbeck College, UK
Laurence Cholvy ONERA - Toulouse, France
Jan Chomicki University at Buffalo, USA
Fabio Cozman Universidade de Sao Paulo, Brazil
Alfredo Cuzzocrea ICAR - CNR, Italy
Michael Dekhtyar Tver State University, Russia
Juergen Dix Clausthal University of Technology, Germany
Didier Dubois IRIT - CNRS, France
Thomas Eiter Vienna University of Technology, Austria
Zied Elouedi LARODEC, ISG de Tunis, Tunisia
Ronald Fagin IBM Research - Almaden, USA
Minos Garofalakis Technical University of Crete, Greece
Lluis Godo Artificial Intelligence Research Institute,
IIIA - CSIC, Spain
Nikos Gorogiannis Middlesex University, UK
John Grant Towson University, USA
Sergio Greco University of Calabria, Italy
Stijn Heymans SRI International, USA
Anthony Hunter University College London, UK
Gabriele Kern-Isberner Technische Universitaet Dortmund, Germany
Angelika Kimmig KU Leuven, Belgium
Kathryn Laskey George Mason University, USA
Jonathan Lawry University of Bristol, UK
Sebastian Link The University of Auckland, New Zealand
Peter Lucas Radboud University Nijmegen,
The Netherlands
Jianbing Ma Bournemouth University, UK
Thomas Meyer Centre for Artificial Intelligence Research,
UKZN and CSIR Meraka, South Africa
Serafin Moral University of Granada, Spain
Kedian Mu School of Mathematical Sciences,
Peking University, China
VIII Organization
Jeff Z. Pan University of Aberdeen, UK
Simon Parsons University of Liverpool, UK
Rafael Pen˜aloza TU Dresden, Germany
Olivier Pivert IRISA - ENSSAT, France
Henri Prade IRIT - CNRS, France
Andrea Pugliese DEIS - University of Calabria, Italy
Guilin Qi Southeast University, China
Fabrizio Riguzzi University of Ferrara, Italy
Sebastian Rudolph Technische Universita¨t Dresden, Germany
V´ıtor Santos Costa Universidade do Porto, Portugal
Steven Schockaert Cardiff University, UK
Guillermo Ricardo Simari Universidad Nacional del Sur in Bahia Blanca,
Argentina
Giorgos Stoilos National Technical University of Athens
(NTUA), Greece
Umberto Straccia ISTI - CNR, Italy
Heiner Stuckenschmidt University of Mannheim, Germany
Vicenc Torra IIIA - CSIC, Spain
Peter Vojt´aˇs Charles University, Czech Republic
Nic Wilson 4C, UCC, Cork, Ireland
Ronald Yager Machine Intelligence Institute - Iona College,
USA
Additional Reviewers
Britz, Arina Molinaro, Cristian
Casini, Giovanni Spina, Cinzia Incoronata
Garcia, Jhonatan Tsalapati, Eleni
Klarman, Szymon Weinzierl, Antonius
Meilicke, Christian
Table of Contents
Possibilistic Networks: A New Setting for Modeling Preferences ........ 1
Nahla BenAmor, Didier Dubois, H´ela Gouider, and Henri Prade
Min-based Assertional Merging Approach for Prioritized DL-Lite
Knowledge Bases ................................................ 8
Salem Benferhat, Zied Bouraoui, Sylvain Lagrue, and Julien Rossit
On the Revision of Prioritized DL-Lite Knowledge Bases.............. 22
Salem Benferhat, Zied Bouraoui, and Karim Tabia
Interval-Based Possibilistic Networks ............................... 37
Salem Benferhat, Sylvain Lagrue, and Karim Tabia
Tractable vs. Intractable Cases of Query Answering under Matching
Dependencies.................................................... 51
Leopoldo Bertossi and Jaffer Gardezi
Analogical Classification: Handling Numerical Data .................. 66
Myriam Bounhas, Henri Prade, and Gilles Richard
Lazy Analogical Classification: Optimization and Precision Issues ...... 80
William Correa Beltran, H´el`ene Jaudoin, and Olivier Pivert
Polyhedral Labellings for Argumentation Frameworks ................ 86
Cosmina Croitoru
Update Operators for Inconsistent Query Answering: A New Point
of View......................................................... 100
Madalina Croitoru and Ricardo Oscar Rodriguez
Conflicts of Belief Functions: Continuity and Frame Resizement........ 106
Milan Daniel and Jianbing Ma
Improving Inconsistency Resolution by Considering Global Conflicts.... 120
Cristhian Ariel David Deagustini, Maria Vanina Mart´ınez,
Marcelo A. Falappa, and Guillermo Ricardo Simari
Probabilistic Argumentation Frameworks – A Logical Approach........ 134
Dragan Doder and Stefan Woltran
Computing Skyline from Evidential Data ........................... 148
Sayda Elmi, Karim Benouaret, Allel Hadjali,
Mohamed Anis Bach Tobji, and Boutheina Ben Yaghlane
X Table of Contents
A Two-Level Approach to Maximum Entropy Model Computation
for Relational Probabilistic Logic Based on Weighted Conditional
Impacts......................................................... 162
Marc Finthammer and Christoph Beierle
Solving Hidden-Semi-Markov-Mode Markov Decision Problems ........ 176
Emmanuel Hadoux, Aur´elie Beynier, and Paul Weng
Probabilistic Strategies in Dialogical Argumentation.................. 190
Anthony Hunter
Analytics over Probabilistic Unmerged Duplicates.................... 203
Ekaterini Ioannou and Minos Garofalakis
A PsychologicalAnalysis of Preference Semantics in Conditional Logics
for Preference Representation...................................... 209
Souhila Kaci and Eric Raufaste
Answering Ontological Ranking Queries Based on Subjective Reports... 223
Thomas Lukasiewicz, Maria Vanina Mart´ınez, Cristian Molinaro,
Livia Predoiu, and Gerardo I. Simari
A Petri Net Model of Argumentation Dynamics...................... 237
Diego C. Martinez, Maria Laura Cobo, and
Guillermo Ricardo Simari
Integrity Constraints for Probabilistic Spatio-Temporal
Knowledgebases ................................................. 251
Francesco Parisi and John Grant
Repairs and Consistent Answers for Inconsistent Probabilistic
Spatio-Temporal Databases ....................................... 265
Francesco Parisi and John Grant
Skyline Queries in an Uncertain Database Model Based on Possibilistic
Certainty ....................................................... 280
Olivier Pivert and Henri Prade
Querying Uncertain Multiple Sources............................... 286
Olivier Pivert and Henri Prade
Population Size Extrapolation in Relational Probabilistic Modelling .... 292
David Poole, David Buchman, Seyed Mehran Kazemi,
Kristian Kersting, and Sriraam Natarajan
Improving PersonalizedSearchon the Social Web Based on Similarities
between Users ................................................... 306
Zhenghua Xu, Thomas Lukasiewicz, and Oana Tifrea-Marciuska
Author Index.................................................. 321
Possibilistic Networks:
A New Setting for Modeling Preferences
NahlaBenAmor1,DidierDubois2,He´laGouider1,andHenriPrade2
1 LARODEC,Universite´deTunis,ISGdeTunis,41ruedelaLiberte´,2000LeBardo,Tunisia
2 IRIT–CNRS,118,routedeNarbonne,Toulouse,France
[email protected], {dubois,prade}@irit.fr,
[email protected]
Abstract. PossibilisticnetworksarethecounterpartofBayesiannetworksinthe
possibilisticsetting.Possibilisticnetworkshaveonlybeenstudiedanddeveloped
from a reasoning-under-uncertainty point of view until now. In thisshort note,
for the first time, one advocates their interest in preference modeling. Beyond
theirgraphicalappeal,theycanbeshowntoprovideanaturalencodingofpref-
erencesagreeingwiththeinclusion-basedpartialorderappliedtothesubsetsof
preferencesviolatedinthedifferentsituations.Moreovertheydonotencounter
thelimitationsofCP-Netsintermsofrepresentationcapabilities.Theyalsoenjoy
alogicalcounterpartthatmaybeusedforconsistencychecking.Thisshortnote
provides a comparative discussion of the merits of possibilistic networks with
respecttootherexistingpreferencemodelingframeworks.
1 Introduction
Preferencesareusuallyexpressedbymeansoflocalpiecesofinformation,ratherthan
as a complete preorder between the different possible states of the world. This state
of facts has led AI researchers to propose compactrepresentation formats for prefer-
encesandproceduresforcomputingaplausiblerankingbetweencompletelydescribed
situations from such representations, in the last fifteen years. Conditional preference
networks[6](CP-Netsforshort)haveemergedasapopularreferencesettingforrepre-
sentingpreferences,leadingtodifferentrefinements[5,15],aswellassomealternative
approaches[4,8,13] (see [7] for a brief overview). Inspired from Bayesian networks,
CP-Netsinherittheirgraphicalnature,andbesides,relyonasimple,apparentlynatural
principle,namedceterisparibus,whichallowstoextendanycontextualpreference“in
contextc,Ipreferato¬a”(denotedforshortc:a(cid:2)¬a),toanyparticularspecification
boftheothervariablesusedfordescribingtheconsideredsituations,i.e.,thepreference
is understood as ∀b,cab is preferred to c¬ab. The CP-net approach perfectly exem-
plifiestheingredientsneededforasatisfactoryrepresentationofpreferences,statedin
a conditionalmanner,into a partial order useful for a user: i) a simple representation
setting,preferablyhavingagraphicalcounterpartforelicitationease,ii)anaturalprin-
cipleformakingexplicitthepreferencesbetweencompletelydescribedsituations,and
iii)analgorithmfordetermininghowtocomparetwocompletesituationsaccordingto
theexistenceofapathofworseningflipslinkingthem.Inspiteoftheirappealingfea-
tures,CP-Nets havesomelimitations.First, there existpreordersthatmakesense and
U.StracciaandA.Cal`ı(Eds.):SUM2014,LNAI8720,pp.1–7,2014.
(cid:2)c SpringerInternationalPublishingSwitzerland2014