Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges (Studies on the Semantic Web)

314 Pages·2020·11.32 MB·English

by  I, I. (editor)

KNOWLEDGE GRAPHS FOR EXPLAINABLE ARTIFICIAL INTELLIGENCE:
FOUNDATIONS, APPLICATIONS AND CHALLENGES
Studies on the Semantic Web

Semantic Web has grown into a mature field of research. Its methods find innovative applica-
tions on and off the World Wide Web. Its underlying technologies have significant impact on
adjacent fields of research and on industrial applications. This book series reports on the state
of the art in foundations, methods, and applications of Semantic Web and its underlying
technologies. It is a central forum for the communication of recent developments and com-
prises research monographs, textbooks and edited volumes on all topics related to the Se-
mantic Web.

Editor-in-Chief:
Prof. Dr. Pascal Hitzler
Department of Computer Science, Kansas State University, Manhattan, KS 66502, USA
Email: [email protected]

Editorial Board:
Diego Calvanese, Vinary Chaudhri, Fabio Ciravegna, Michel Dumontier, Dieter Fensel,
Fausto Giunchiglia, Carole Goble, Asunción Gómez Pérez, Frank van Harmelen,
Manfred Hauswirth, Ian Horrocks, Krzysztof Janowicz, Michael Kifer, Riichiro Mizoguchi,
Mark Musen, Daniel Schwabe, Barry Smith, Steffen Staab, Rudi Studer and Elena Simperl
Volume 047
Previously published in this series:

Vol. 046 Daniel Dominik Janke, Study on Data Placement Strategies in Distributed RDF Stores
Vol. 045 Pavlos Vougiouklis, Neural Generation of Textual Summaries from Knowledge Base
Triples
Vol. 044 Diego Collarana, Strategies and Techniques for Federated Semantic Knowledge
Integration and Retrieval
Vol. 043 Filip Ilievski, Identity of Long-Tail Entities in Text
Vol. 042 Fariz Darari, Managing and Consuming Completeness Information for RDF Data
Sources
Vol. 041 Steffen Thoma, Multi-Modal Data Fusion Based on Embeddings
Vol. 040 Marilena Daquino, Mining Authoritativeness in Art Historical Photo Archives.
Semantic Web Applications for Connoisseurship
Vol. 039 Bo Yan, Geographic Knowledge Graph Summarization
Vol. 038 Petar Ristoski, Exploiting Semantic Web Knowledge Graphs in Data Mining
Vol. 037 Maribel Acosta Deibe, Query Processing over Graph-structured Data on the Web
Vol. 036 E. Demidova, A.J. Zaveri, E. Simperl (Eds.), Emerging Topics in Semantic Technologies
Vol. 035 Giuseppe Cota, Inference and Learning Systems for Uncertain Relational Data
Vol. 034 Ilaria Tiddi, Explaining Data Patterns using Knowledge from the Web of Data
Vol. 033 Anne E. Thessen, Application of Semantic Technology in Biodiversity Science
Vol. 032 Pascal Hitzler et al. (Eds.), Advances in Ontology Design and Patterns
Vol. 031 Michael Färber, Semantic Search for Novel Information
Vol. 030 Hassan Saif, Semantic Sentiment Analysis in Social Streams
Vol. 029 A. Ławrynowicz, Semantic Data Mining: An Ontology-Based Approach
Vol. 028 R. Zese, Probabilistic Semantic Web: Reasoning and Learning
Vol. 027 M. Kejriwal, Populating a Linked Data Entity Name System

ISSN 1868-1158 (print)
ISSN 2215-0870 (online)
KNOWLEDGE GRAPHS FOR
EXPLAINABLE ARTIFICIAL
INTELLIGENCE: FOUNDATIONS,
APPLICATIONS AND CHALLENGES
Edited by
Ilaria Tiddi
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Freddy Lécué
Thales, Montreal, Canada & Inria, Sophia Antipolis, France
and
Pascal Hitzler
Kansas State University, Manhattan, Kansas, USA
© 2020 Akademische Verlagsgesellschaft AKA GmbH, Berlin
All rights reserved. No part of this book may be reproduced, stored in a retrieval system,
or transmitted, in any form or by any means, without prior written permission from the publisher.
ISBN 978-3-89838-754-5 (AKA, print)
ISBN 978-1-64368-080-4 (IOS Press, print)
ISBN 978-1-64368-081-1 (IOS Press, online)
doi: 10.3233/SSW47
Bibliographic information available from the Katalog der Deutschen Nationalbibliothek (German
National Library Catalogue) at https://www.dnb.de
Publisher
Akademische Verlagsgesellschaft AKA GmbH, Berlin
Represented by Co-Publisher IOS Press
IOS Press BV
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: [phone]
Fax: [phone]
email: [email protected]
LEGAL NOTICE
The publisher is not responsible for the use which might be made of the following information.
PRINTED IN THE NETHERLANDS
v
Preface
Explanations have been the subject of study in a variety of fields for a long time, and
are experiencing a new wave of popularity due to the latest advancements in Artificial
Intelligence(AI).Whilemachineanddeeplearningsystemsarenowwidelyadoptedfor
decision making, they also revealed a major drawback, namely the inability to explain
theirdecisionsinawaythathumanscaneasilyunderstandthem.Asaresult,eXplainable
AI(XAI)rapidlybecameanactiveareaofresearchinresponsetotheneedofimproving
the understandability and trustworthiness of modern AI systems – a crucial aspect for
theiradoptionatlargescale,andparticularlyinlife-criticalcontexts.
The field of Knowledge Representation and Reasoning (KRR), on the other hand,
has a long standing tradition in managing structured knowledge, i.e. modeling, creat-
ing,standardising,publishingandsharinginformationinsymbolicform.KRRmethods
andtechnologiesdevelopedovertheyearsresultbynowinlargeamountsofstructured
knowledge(intheformofontologies,knowledgegraphs,andotherstructuredrepresenta-
tions),thatarenotonlymachine-readableandinstandardformats,butalsoopenlyavail-
able,andcoveringavarietydomainsatlargescale.Thesestructuredsources,designedto
capturecausationasopposedtocorrelationinMachineLearningmethods,couldthere-
fore be exploited as sources of background knowledge by eXplainable AI methods in
ordertobuildmoreinsightful,trustworthyexplanations.
Thisbookprovidestheveryfirstcomprehensivecollectionofresearchcontributions
ontheroleofknowledgegraphsforeXplainableAI(KG4XAI).Wegatherstudiesusing
KRR as a framework to enable intelligent systems to explain their decisions in a more
understandableway,presentingacademicandindustrialresearchfocusedonthetheory,
methodsandimplementationsofAIsystemsthatusestructuredknowledgetogenerate
reliableexplanations.
We include both introductory material on knowledge graphs for readers with only
aminimalbackgroundinthefield,andadvancedspecificchaptersdevotedtomethods,
applicationsandcase-studiesusingknowledgegraphsasapartofknowledge-based,ex-
plainablesystems(KBX-systems).Thefinalchaptersconveycurrentchallengesandfu-
turedirectionsofresearchintheareaofknowledgegraphsforeXplainableAI.
Ourgoalisnotonlytoprovideascholarly,state-of-the-artoverviewofresearchin
thisfield,butalsotofosterthehybridcombinationofsymbolicandandsubsymbolicAI
methods,motivatedbythecomplementarystrengthsandlimitationsofboththefieldof
KRRandMachineLearning.
The editors would like to thank all contributing authors for their efforts in making this
bookpossible.
March2020 IlariaTiddi
FreddyLe´cue´
PascalHitzler
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vii

Contents
Preface v
Ilaria Tiddi, Freddy Lécué and Pascal Hitzler
Part 1. Foundations of Knowledge-Based eXplainable Systems
Knowledge Graphs on the Web – An Overview 3
Nicolas Heist, Sven Hertling, Daniel Ringler and Heiko Paulheim
Foundations of Explainable Knowledge-Enabled Systems 23
Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne
and Deborah L. McGuinness
Knowledge Graph Embeddings and Explainable AI 49
Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari
and Pasquale Minervini
Benchmarking the Lifecycle of Knowledge Graphs 73
Michael Röoder, Mohamed Ahmed Sherif, Muhammad Saleem, Felix Conrads
and Axel-Cyrille Ngonga Ngomo
Part 2. Applications
Knowledge-Aware Interpretable Recommender Systems 101
Vito Walter Anelli, Vito Bellini, Tommaso Di Noia and Eugenio Di Sciascio
Differentiable Reasoning on Large Knowledge Bases and Natural Language 125
Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel
and Edward Grefenstette
Neuro-Symbolic Architectures for Context Understanding 143
Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma
and Ruwan Wickramarachchi
Knowledge Representation and Reasoning Methods to Explain Errors in Machine
Learning 161
Marjan Alirezaie, Martin Längkvist and Amy Loutfi
Knowledge-Based Explanations for Transfer Learning 180
Freddy Lécué, Jiaoyan Chen, Jeff Z. Pan and Huajun Chen
Explanations in Predictive Analytics: Case Studies 196
Jiewen Wu, Minh Nguyen, Gia H. Ngo and Nancy F. Chen
Generating Explanations in Natural Language from Knowledge Graphs 213
Diego Moussallem, René Speck and Axel-Cyrille Ngonga Ngomo
viii
Part 3. Challenges for Knowledge-Based eXplainable Systems
Directions for Explainable Knowledge-Enabled Systems 245
Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne
and Deborah L. McGuinness
The Data Ethics Challenges of Explainable AI and Their Knowledge-Based
Solutions 262
Mathieu d’Aquin
Who Is This Explanation for? Human Intelligence and Knowledge Graphs
for eXplainable AI 276
Irene Celino
Managing Identity in Knowledge-Based Explainable Systems 286
Ilaria Tiddi and Joe Raad
Subject Index 301
Author Index 303