Engineering Background Knowledge for Social Robots

240 Pages·2020·4.932 MB·English

by  Luigi Asprino

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 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 tech-
nologies. 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: [e-mail]

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
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Abstract
Social robots are embodied agents that continuously perform knowl-
edge-intensivetasksinvolvingseveralkindsofinformationcomingfrom
different heterogeneous sources. Providing a framework for engineer-
ing 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 so-
cial 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.