Graph Structures for Knowledge Representation and Reasoning: 5th International Workshop, GKR 2017, Melbourne, VIC, Australia, August 21, 2017, Revised Selected Papers PDF
Preview Graph Structures for Knowledge Representation and Reasoning: 5th International Workshop, GKR 2017, Melbourne, VIC, Australia, August 21, 2017, Revised Selected Papers
Madalina Croitoru · Pierre Marquis Sebastian Rudolph · Gem Stapleton (Eds.) 5 Graph Structures 7 7 0 for Knowledge Representation 1 I A and Reasoning N L 5th International Workshop, GKR 2017 Melbourne, VIC, Australia, August 21, 2017 Revised Selected Papers 123 fi Lecture Notes in Arti cial Intelligence 10775 Subseries of Lecture Notes in Computer Science LNAI Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany More information about this series at http://www.springer.com/series/1244 Madalina Croitoru Pierre Marquis (cid:129) Sebastian Rudolph Gem Stapleton (Eds.) (cid:129) Graph Structures for Knowledge Representation and Reasoning 5th International Workshop, GKR 2017 Melbourne, VIC, Australia, August 21, 2017 Revised Selected Papers 123 Editors Madalina Croitoru Sebastian Rudolph LIRMM Technische UniversitätDresden Montpellier Cedex5 Dresden France Germany Pierre Marquis GemStapleton CRIL-CNRS andUniversitéd’Artois University of Brighton Lens Brighton France UK ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Artificial Intelligence ISBN 978-3-319-78101-3 ISBN978-3-319-78102-0 (eBook) https://doi.org/10.1007/978-3-319-78102-0 LibraryofCongressControlNumber:2018937369 LNCSSublibrary:SL7–ArtificialIntelligence ©SpringerInternationalPublishingAG,partofSpringerNature2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbytheregisteredcompanySpringerInternationalPublishingAG partofSpringerNature Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Versatile and effective techniques for knowledge representation and reasoning (KRR) are essential for the development of successful intelligent systems. Many rep- resentatives of next-generation KRR systems are based on graph-based knowledge representation formalismsandleveragegraph-theoreticalnotionsandresults. Thegoal of the workshop series on Graph Structures for Knowledge Representation and Rea- soning (GKR) is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques. This volume contains extended and revised selected papers of the fifth edition of GKR, which took place in Melbourne, Australia, on August 21, 2017. Like the pre- viouseditions,heldinPasadena,USA(2009),Barcelona,Spain(2011),Beijing,China (2013),andBuenosAires,Argentina(2015),theworkshopwasassociatedwithIJCAI (theInternationalJointConferenceonArtificialIntelligence),thusprovidingtheperfect venue for a rich and valuable exchange. Beside the extended workshop papers, this volume also contains two invited contributions of core GKR community members. The scientific program of this workshop included many topics related to graph-based knowledge representation and reasoning such as argumentation, concep- tual graphs, formal concept analysis, graphical models, Bayesian networks, concept diagrams, and many more. All in all, the fifth edition of the GKR workshop was very successful. The papers coming from diverse fields all addressed various issues in knowledge representation and reasoning and the common graph-theoretic background allowedustobridgethegapbetweenthedifferentcommunities.Thismadeitpossible for the participants to gain new insights and inspiration. We are grateful for the support of IJCAI and we would also like to thank the Program Committee of the workshop for their hard work in reviewing papers and providing valuable guidance to the contributors. But, of course, GKR 2017 would not have been possible without the dedicated involvement of the contributing authors and participants. February 2018 Madalina Croitoru Pierre Marquis Sebastian Rudolph Gem Stapleton Organization Workshop Chairs Madalina Croitoru LIRMM, Université Montpellier II, France Pierre Marquis CRIL-CNRS and Université d’Artois, France Sebastian Rudolph Technische Universität Dresden, Germany Gem Stapleton University of Brighton, UK Program Committee Simon Andrews Sheffield Hallam University, UK Abdallah Arioua INRA, LIRMM, Université Montpellier II, France Zied Bouraoui Cardiff University, UK Dan Corbett Optimodal Technologies, USA Olivier Corby Inria, France Cornelius Croitoru University Al.I.Cuza Iaşi, Romania Frithjof Dau SAP, Germany Juliette Dibie-Barthélemy AgroParisTech, France Peter Eklund IT University of Copenhagen, Denmark Catherine Faron Zucker Université Nice Sophia Antipolis, France Sebastien Ferre Université de Rennes 1, France Christophe Gonzales LIP6, Université Paris 6, France Ollivier Haemmerlé IRIT, Université Toulouse le Mirail, France John Howse University of Brighton, UK Bernard Moulin Université Laval, Canada Laura Papaleo Université Paris-Sud, France Simon Polovina Sheffield Hallam University, UK Uta Priss Ostfalia University, Germany Karim Tabia Université d’Artois, France Srdjan Vesic CRIL, CNRS – Université d’Artois, France Nic Wilson Insight UCC, Cork, Ireland Stefan Woltran Vienna University of Technology, Austria Bruno Yun Université Montpellier II, France Additional Reviewers Jan Maly Vienna University of Technology, Austria Axel Polleres Vienna University of Economics and Business, Austria Contents Extended Workshop Papers Exploring, Reasoning with and Validating Directed Graphs by Applying Formal Concept Analysis to Conceptual Graphs. . . . . . . . . . . . 3 Simon Andrews and Simon Polovina Subjective Bayesian Networks and Human-in-the-Loop Situational Understanding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Dave Braines, Anna Thomas, Lance Kaplan, Murat Şensoy, Jonathan Z. Bakdash, Magdalena Ivanovska, Alun Preece, and Federico Cerutti Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Tanya Braun and Ralf Möller Representing and Reasoning About Logical Network Topologies . . . . . . . . . 73 Shaun Voigt, Catherine Howard, Dean Philp, and Christopher Penny From Enterprise Concepts to Formal Concepts: A University Case Study. . . . 84 Jamie Caine and Simon Polovina Invited Contributions Visualizing ALC Using Concept Diagrams. . . . . . . . . . . . . . . . . . . . . . . . . 99 Gem Stapleton, Aidan Delaney, Michael Compton, and Peter Chapman Graph Theoretical Properties of Logic Based Argumentation Frameworks: Proofs and General Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Bruno Yun, Madalina Croitoru, Srdjan Vesic, and Pierre Bisquert Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Extended Workshop Papers Exploring, Reasoning with and Validating Directed Graphs by Applying Formal Concept Analysis to Conceptual Graphs B Simon Andrews and Simon Polovina( ) Conceptual Structures Research Group, Department of Computing, Communication and Computing Research Centre, Sheffield Hallam University, Sheffield, UK {s.andrews,s.polovina}@shu.ac.uk Abstract. Althoughtoolsexisttoaidpractitionersintheconstruction of directed graphs typified by Conceptual Graphs (CGs), it is still quite possible for them to draw the wrong model, mistakenly or otherwise. In larger or more complex CGs it is furthermore often difficult–without closeinspection–toseeclearlythekeyfeaturesofthemodel.Thispaper thereby presents a formal method, based on the exploitation of CGs as directedgraphsandtheapplicationofFormalConceptAnalysis(FCA). FCAelucidateskeyfeaturesofCGssuchaspathwaysanddependencies, inputs and outputs, cycles, and joins. The practitioner is consequently empowered in exploring, reasoning with and validating their real-world models. 1 Introduction A directed graph–or “digraph”–is a graph whose edges have direction and are called arcs [9,11]. Arrows on the arcs are used to encode the directional infor- mation: an arc from vertex A to vertex B indicates that one may move from A toBbutnotfromBtoA.Suchgraphsforexampleareusedincomputerscience as a representation of the paths that might be traversed through a program, or in higher-level conceptual models where concepts are related to each other by relations that gain additional semantics (i.e. meaning) by defining the direction between the source and target concepts. A classic illustration is a cat that sits onamat[18].Inthissimpleexample‘sits-on’isthesemanticrelationwherethe direction goes from cat to mat and not vice versa. CGs (Conceptual Graphs) are digraphs that enable modellers to express meaning in a form that is logically precise whilst being humanly readable, and serve as an intermediate language for translating between computer-oriented formalisms andnaturallanguages [14,19].CGsgraphicalrepresentationthereby serveasareadable,butformalspecificationlanguageforsystemsdesignorother practitioners using this approach [10]. CGs are however drawn by hand. Tools such as CoGui and CharGer already exist to assist the practitioner in creating a well-formed CG (Conceptual Graph) that adheres to the prescribed grammar andsyntax[1,2].HoweverthereisnoguaranteethatamodelcreatedusingCGs (cid:2)c SpringerInternationalPublishingAG,partofSpringerNature2018 M.Croitoruetal.(Eds.):GKR2017,LNAI10775,pp.3–28,2018. https://doi.org/10.1007/978-3-319-78102-0_1
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