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ENGINEERING BACKGROUND KNOWLEDGE FOR SOCIAL ROBOTS Studies on the Semantic Web Semantic Web has grown into a mature field of research. Its methods find innovative applications 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 comprises research monographs, textbooks and edited volumes on all topics related to the Semantic 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 048 Previously published in this series: Vol. 047 Ilaria Tiddi, Freddy Lécué, Pascal Hitzler (Eds.), Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges 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 ISSN 1868-1158 (print) ISSN 2215-0870 (online) ENGINEERING BACKGROUND KNOWLEDGE FOR SOCIAL ROBOTS Luigi Asprino University of Bologna, Bologna, Italy © 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 978-3-89838-757-6 (AKA, print) ISBN 978-1-64368-108-5 (IOS Press, print) ISBN 978-1-64368-109-2 (IOS Press, online) doi: 10.3233/SSW48 Bibliographic information available from the Katalog der Deutschen Nationalbibliothek (German National Library Catalogue) at https://www.dnb.de Dissertation, approved by the University of Bologna Date of the defense: 3 April 2019 Supervisors: Paolo Ciancarini and Valentina Presutti Publisher Akademische Verlagsgesellschaft AKA GmbH, Berlin Represented by Co-Publisher IOS Press IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam The Netherlands Tel: +31 20 688 3355 Fax: +31 20 687 0019 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 To Serena Thispageintentionallyleftblank Abstract Social robots are embodied agents that continuously perform knowledge-intensivetasksinvolvingseveralkindsofinformationcomingfrom different heterogeneous sources. Providing a framework for engineering robots’ knowledge raises several problems like identifying sources of information and modeling solutions suitable for robots’ activities, integrating knowledge coming from different sources, evolving this knowledge with information learned during robots’ activities, ground- ing perceptions on robots’ knowledge, assessing robots’ knowledge with respect humans’ one and so on. Semantic Web research has faced with most of these issues and can provide robots with the means for creating, organizing, querying and reasoning over knowledge. In fact, Semantic Web standards allow to easily integrate data generated by a variety of components, thus enabling robots to make decisions by taking into account knowledge about physical world, data coming from their operating environment, information about social norms, users’ preferences and so on. Semantic Web technologies provide flexible solutions that allow to extend and evolve robots’ knowledge over time. Linked (Open) Data paradigm (a result of research in the Semantic Web field) lets to easily reuse (i.e. integrate with robots’ knowledge) existing external datasets so to bootstrap a robot’s knowledge base with relevant information for its activities. Linked Data also provides a mechanism that allows robots to mutually share knowledge. Existing solutions for managing robots’ knowledge only partially exploit the potential of Semantic Web technologies and Linked Data. This thesis introduces a component-based architecture relying on Semantic Web standards for supporting knowledge-intensive tasks performed by social robots, and whose design has been guided by requirements coming from a real socially assistive robotic application. All the components contribute to and benefit from the knowledge base which is the corner- vii viii ABSTRACT stone of the architecture. The knowledge base is structured by a set of interconnected and modularized ontologies which are meant to model information relevant for supporting robots in their daily activities. The knowledge base is originally populated with linguistic, ontological and factual knowledge retrieved from the Linked Open Data. The access to the knowledge base is guaranteed by Lizard, a tool that provides software components with an API for accessing facts stored in the knowledge base in a programmatic and object-oriented way. This thesis also introduces two methods for engineering knowledge needed by robots: (i) A novel method for automatically integrating knowledge coming from heterogeneous sources with a frame-driven approach. (ii) A novel empirical method for assessing foundational distinctions over Linked Open Data entities from a common sense perspective (e.g. deciding if an entity inherently represents a class or an instance from a common sense perspective). These methods realize two tasks of a more general procedure meant to automatically evolve robots’ knowledge by automatically integrating information coming from heterogeneous sources, and to generate common sense knowledge by using Linked Open Data as empirical basis. Feasib- ility and benefits of this architecture have been assessed through a prototype deployed in a real socially assistive scenario, whose this thesis presents two applications and the results of a qualitative and quantitative evaluation.