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Reasoning Web. Declarative Artificial Intelligence: 16th International Summer School 2020, Oslo, Norway, June 24–26, 2020, Tutorial Lectures PDF

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Marco Manna · Andreas Pieris (Eds.) l a i r o t u T 8 5 2 Reasoning Web 2 1 S C N Declarative Artificial Intelligence L 16th International Summer School 2020 Oslo, Norway, June 24–26, 2020 Tutorial Lectures Lecture Notes in Computer Science 12258 Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA More information about this series at http://www.springer.com/series/7409 Marco Manna Andreas Pieris (Eds.) (cid:129) Reasoning Web fi Declarative Arti cial Intelligence 16th International Summer School 2020 – Oslo, Norway, June 24 26, 2020 Tutorial Lectures 123 Editors MarcoManna Andreas Pieris Department ofMathematics Schoolof Informatics andComputer Science University of Edinburgh University of Calabria Edinburgh,UK Rende,Italy ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Computer Science ISBN 978-3-030-60066-2 ISBN978-3-030-60067-9 (eBook) https://doi.org/10.1007/978-3-030-60067-9 LNCSSublibrary:SL3–InformationSystemsandApplications,incl.Internet/Web,andHCI ©SpringerNatureSwitzerlandAG2020 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, expressed or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface The Reasoning Web series of annual summer schools has become the prime educa- tionaleventinthefieldofreasoningtechniquesontheWeb,attractingbothyoungand established researchers since its first initiation in 2005 by the European Network of Excellence (REWERSE). This year’s summer school was part of Declarative AI 2020 (https://2020.declarativeai.net) that brought together the 4th International Joint Con- ference on Rules and Reasoning (RuleML+RR 2020), DecisionCAMP 2020, and the 16th Reasoning Web Summer School (RW 2020). Declarative AI 2020 was co-organizedbySINTEFAS,UniversityofOslo,andNorwegianUniversityofScience and Technology, under the umbrella of the SIRIUS Centre for Scalable Data Access. DeclarativeAI 2020was originally plannedtotake placeinthebeautifulcityofOslo, but due to the COVID-19 pandemic, it was held as a virtual event. The broad theme of this year’s summer school was “Declarative Artificial Intelligence” and it covered various aspects of ontological reasoning and related issues that are of particularinteresttoSemanticWebandLinkedDataapplications.Thefollowingeight lectures have been presented during the school (further details can be found at the website of the school (https://2020.declarativeai.net/events/rw-summer-school/rw- lectures): 1. Introduction to Probabilistic Ontologies by Rafael Peñaloza (University of Milano-Bicocca, Italy). 2. On the Complexity of Learning Description Logic Ontologies by Ana Ozaki (University of Bergen, Norway). 3. Explanation via Machine Arguing by Oana Cocarascu (Imperial College London, UK), Antonio Rago (Imperial College London, UK), and Francesca Toni (Imperial College London, UK). 4. Stream Reasoning: From Theory to Practice by Emanuele Della Valle (Politecnico di Milano, Italy), Emanuele Falzone (Politecnico di Milano, Italy), and Riccardo Tommasini (University of Tartu, Estonia). 5. Temporal Ontology-Mediated Queries and First-Order Rewritability: a Short Course by Vladislav Ryzhikov (Birkbeck University of London, UK), Przemysław A. Wałęga (University of Oxford, UK), and Michael Zakharyaschev (Birkbeck University of London, UK). 6. An Introduction to Answer Set Programming and Some of Its Extensions by Wolfgang Faber (University of Klagenfurt, Austria). vi Preface 7. Declarative Data Analysis using Limit Datalog Programs by Egor V. Kostylev (University of Oxford, UK). 8. Knowledge Graphs: Research Directions by Aidan Hogan (University of Chile, Chile). This volume contains the lecture notes that complement the above lectures. All the articles are of high quality and have been written as accompanying material for the studentsofthesummerschool,inordertodeepentheirunderstandingandtoserveasa reference for further detailed study. Further material, such as lecture slides and recordings, can be found at the website of the school. We would like to thank everybody who helped make this event possible. As teaching is the main focus of a summer school, we would first like to thank all the lecturers: your hard work and commitment led to a successful event. We are also thankful to the members of the Scientific Advisory Board: your timely feedback concerningthetechnicalprogram,aswell asforthesubmitted lecturenotes,helpedto organizeahigh-qualityevent.Finally,weexpressourgratitude tothelocal organizers for their constant support. August 2020 Marco Manna Andreas Pieris Organization General Chairs Marco Manna University of Calabria, Italy Andreas Pieris The University of Edinburgh, UK Scientific Advisory Board Leopoldo Bertossi Universidad Adolfo Ibáñez, Chile Thomas Eiter TU Wien, Austria Birte Glimm Ulm University, Germany Markus Krötzsch TU Dresden, Germany Yuliya Lierler University of Nebraska Omaha, USA Carsten Lutz University of Bremen, Germany Emanuel Sallinger University of Oxford, UK Contents Introduction to Probabilistic Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Rafael Peñaloza On the Complexity of Learning Description Logic Ontologies . . . . . . . . . . . 36 Ana Ozaki Explanation via Machine Arguing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Oana Cocarascu, Antonio Rago, and Francesca Toni Stream Reasoning: From Theory to Practice. . . . . . . . . . . . . . . . . . . . . . . . 85 Emanuele Falzone, Riccardo Tommasini, and Emanuele Della Valle Temporal Ontology-Mediated Queries and First-Order Rewritability: A Short Course. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 VladislavRyzhikov,PrzemysławA.Wałęga,andMichaelZakharyaschev An Introduction to Answer Set Programming and Some of Its Extensions . . . 149 Wolfgang Faber Declarative Data Analysis Using Limit Datalog Programs . . . . . . . . . . . . . . 186 Egor V. Kostylev Knowledge Graphs: Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Aidan Hogan Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Introduction to Probabilistic Ontologies B Rafael Pen˜aloza( ) IKR3 Lab, University of Milano-Bicocca, Milan, Italy [email protected] Abstract. There is no doubt about it; an accurate representation of a real knowledge domain must be able to capture uncertainty. As the best known formalism for handling uncertainty, probability theory is oftencalleduponthistask,givingrisetoprobabilisticontologies.Unfor- tunately, things are not as simple as they might appear, and different choicesmadecandeeplyaffectthesemanticsandcomputationalproper- ties of probabilistic ontology languages. In this tutorial, we explore the main design choices available, and the situations in which they may be meaningful or not. We then dive deeper into a specific family of prob- abilistic ontology languages which can express logical and probabilistic dependencies between axioms. 1 Introduction Ontologies, in the computer-science sense of “representations of a knowledge domain” present a prominent research area of interest within the general umbrella of Artificial Intelligence, and more precisely in Knowledge Represen- tation. Indeed, any intelligent agent should have, in one way or another, a rep- resentation of the knowledge of its active domain, so that it is able to react to observations of its environment in an adequate manner. Within the context of the Semantic Web, ontologies have been identified as an appropriate manner to represent, combine, and in general use distributed knowledge from different sources into specific applications. Notably, beyond the generalviewofknowledgeinter-connectivity,manyindustrialplayers,knowledge domains,andotherusersarebuildingspecialisedontologies forfittingtheirown needs. This has led not only to a large collection of ontologies dealing with all sortsofdomainareasandlargerepositoriesholdingthembut,moreimportantly, withaplethoraoflanguageswhichareusedtobuildthem.Abstractingfromthe specific peculiarities of these languages (and to avoid the need to clarify at each pointwhichlanguageistakenunderconsideration),wewillseeanontologybasi- cally as a (typically finite) set of constraints that define how different elements of the knowledge domain should relate to each other. In particular, we focus on a setting where the ontology language has an underlying logical interpretation. The use of logic-based formalisms allows us to provide formal semantics, and guarantees of correctness for entailment methods. Thus, we can speak about consequences that follow from a given ontology; informally, these are pieces of knowledge that are implicitly (rather than explicitly) encoded in the ontology. (cid:2)c SpringerNatureSwitzerlandAG2020 M.MannaandA.Pieris(Eds.):ReasoningWeb2020,LNCS12258,pp.1–35,2020. https://doi.org/10.1007/978-3-030-60067-9_1

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