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

263 Pages·2020·13.562 MB·English

by  Marco Manna, Andreas Pieris

Marco Manna · Andreas Pieris (Eds.)
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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
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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.
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M.MannaandA.Pieris(Eds.):ReasoningWeb2020,LNCS12258,pp.1–35,2020.
https://doi.org/10.1007/978-3-030-60067-9_1