=Paper=
{{Paper
|id=Vol-1935/paper-06
|storemode=property
|title=A Knowledge Management System for Assistive Robotics
|pdfUrl=https://ceur-ws.org/Vol-1935/paper-06.pdf
|volume=Vol-1935
|authors=Luigi Asprino,Aldo Gangemi,Andrea Giovanni Nuzzolese,Valentina Presutti,Alessandro Russo
|dblpUrl=https://dblp.org/rec/conf/esws/AsprinoGNPR17
}}
==A Knowledge Management System for Assistive Robotics==
https://ceur-ws.org/Vol-1935/paper-06.pdf
A Knowledge Management System for Assistive
Robotics
Luigi Asprino1,2 , Aldo Gangemi1,3 Andrea Giovanni Nuzzolese1 ,
Valentina Presutti1 , and Alessandro Russo1
1
STLab, ISTC-CNR, Rome, Italy
{name.lastname}@istc.cnr.it
2
DISI - Università di Bologna, Bologna, Italy
3
LIPN, Université Paris 13, Sorbone Cité, UMR CNRS, Paris, France
Abstract. In this paper we demonstrate how to use an ontology net-
work in order to fill the gap between knowledge and robots’ abilities. The
demonstration is focused on two components of the knowledge manage-
ment system of MARIO robots. These components are the MARIO On-
tology Network (i.e. MON) that organises knowledge in MARIO and an
Object-RDF mapper, called Lizard, that dynamically generates APIs on
top of the MON to enable the interaction between software components
that implement robot’s abilities and the MON itself.
1 Introduction
The MARIO robot4 is an assistive robot that has to support a set of knowledge-
intensive tasks aimed at (i) helping patients affected by dementia to feel more
autonomous and less lonely, (ii) supporting carers in their activity to assess
the patient’s cognitive condition. Examples of knowledge-intensive tasks are the
Comprehensive Geriatric Assessment (CGA) and the triggering of appropriate
entertainment activities. In order to enable this tasks MARIO features a set of
abilities implemented by pluggable software components. MARIO abilities, when
executed, contribute to and benefit from a common knowledge base, which is
modelled according to on an ontology network called MARIO Ontology Network
(MON). In this paper we present two components of the Knowledge Management
System of MARIO robots, i.e. (i) the MARIO Ontology Network (MON) and (ii)
Lizard. The main objective of MON is to provide MARIO robots with the needed
infrastructure to organise knowledge. Instead, the main objective of Lizard is to
provide MARIO robots with APIs dynamically built on top of MON in order to
allow the software components implementing the abilities to access, query and
store the knowledge organised by the MON. The rest of the paper is organised
as follows: (i) Section 2 presents the related work; (ii) Section 3 provides details
about the design methodology adopted for modelling the MON (cf. Section 3.1)
and the knowledge areas covered by the ontology network (cf. Section 3.2); (iii)
Section 4 describes Lizard; finally, (iv) Section 5 provides the conclusions.
4
http://www.mario-project.eu/portal/
Asprino et al.
2 Related work
In recent year many ontology based knowledge systems for robotics have been
proposed. Examples are ORA [6], RACE [8], and OUR-K frame work [4]. ORA
introduces a core ontology that encompasses a set of terms commonly used in
Robotics and Automation along with the methodology we have adopted. RACE
uses an ontology for connecting actuator and sensory experiences with seman-
tics for implementing robots’ abilities. OUR-K presents an ontology-based uni-
fied robot knowledge framework that integrates low-level data with high-level
knowledge for robot intelligence. To the best of our knowledge none of the cited
works uses a modular ontology network to organise knowledge and foster its
reuse for implementing robots’ abilities by means of an Object-RDF mapper
that dynamically provides HTTP REST APIs for interacting with such an on-
tology network.
3 MARIO Ontology Network
In the following sections we describe the methodology adopted for designing the
MON and the resulting ontology modules.
3.1 Design Methodology
The MON is designed by following best design practices and pattern-based
ontology engineering aimed at extensively re-using Ontology Design Patterns
(ODPs) [3] for modelling ontologies. The design methodology that we followed
is based on an extension [7] of the eXtreme Design [2], an agile design method-
ology developed in the context of the NeON project5 . Such an extension mainly
focuses on providing ontology engineer with clear strategies for ontology re-use.
According to the guidelines provided by [7], the strategy we adopted for the
development of the MON is the indirect re-use of ontology design patterns and
alignments. This means that ODPs are used as templates. At the same time,
the ontology guarantees interoperability by keeping the appropriate alignments
with the external ODPs, and provides extensions that satisfy more specific re-
quirements. With this type of reuse, the potential impact of possible changes in
the external ontology modules (e.g. ODPs) is minimised.
3.2 Ontology Network Modules
Based on this methodology we designed the MON as a networked ontology6
composed of different modules that cover different knowledge areas that are
relevant to MARIO in order to make it a cognitive agent able to support older
5
http://www.neon-project.org/nw/.
6
The ontologies part of the network are available at http://www.
ontologydesignpatterns.org/ont/mario/. The root of the network is avail-
able at http://www.ontologydesignpatterns.org/ont/mario/mario.owl.
48 Asprino et al.
patient affected by dementia. The knowledge areas were identified by analysing
the use cases emerged from the system specification carried on for Pilot 1 in
the context of the Work Package 1 [1] of the MARIO project. These uses cases
mainly describe actions and behaviours that the robot should perform or select.
However, they also provide us with detailed descriptions about the nature of the
knowledge that the robot should deal with in order to perform and select actions
and behaviours, respectively. For each use case we highlighted the knowledge
domains required to address the use cases. This process was enabled by the
identification of the competency questions from the textual descriptions of the use
cases. We remark that in knowledge engineering the competency questions are
commonly identified as the requirements that an ontology has to address. Hence,
the knowledge domains emerged from the competency questions we collected,
i.e. the knowledge topics that specific competency questions answers to. Finally,
we gathered a set of top-level knowledge areas by iteratively generalising the
knowledge domains.
Figure 1 shows the ontologies composing the MON. Each color identifies a
specific knowledge area, namely:
– Personal sphere. People information, information about relationship among
people, contacts, etc.;
– Life events. Information about everyday events, patterns memories, schedul-
ing, plans, etc.;
– Social and multimedia content. Online social network community, mul-
timedia content such as photos, videos, movies, documents;
– Environment. Information about rooms, furnitures, objects, etc.;
– Health sphere. Information about living patterns, health patterns, vital
signs, anything related to CGI;
– Emotional sphere. Information about emotions, sentiments, interests, opin-
ions related to people etc.;
– Open knowledge. Speech-derived data, web-extracted data, etc.;
– Regulatory sphere. Information about norms, rules, social habits etc.;
– “MARIOception”. Information about the MARIO robot, its functionali-
ties, and the applications it is able to run and to do.
4 Binding MON with robots’ abilities
The MON organises knowledge in MARIO robots according to the areas de-
scribed in Section 3.2. However, robots’ abilities are implemented in MARIO by
software components that can be plugged-in by means of the REST architec-
tural style. These software components are supposed to interact with the MON
in order to access, store, and dealing with the knowledge organised by the MON.
Hence, MARIO is provided with a Knowledge Management System that relies
on an Object-RDF mapper called Lizard7 . An Object-RDF mapper is a system
7
Lizard is available as open source with Apache Version 2 license on GitHub at
https://github.com/anuzzolese/lizard
Asprino et al.
Fig. 1: Core modules of the MON. Circles and arrows represent ontologies and
owl:import axioms, respectively.
that exposes the RDF triple sets as sets of resources and seamlessly integrates
them into the Object Oriented paradigm. However, differently from existing sys-
tems such as SuRF8 or ActiveRDF9 [5], Lizard provides a RESTful layer that
exposes Object Oriented paradigm by using the REST architectural style over
HTTP. Basically, Lizard dinamically generates Java and HTTP REST APIs
from the MARIO Ontology Network (MON). Those APIs reflect the semantics
of MON and allow transparent access to the knowledge base. This means that
a client application (i.e., any component of the MARIO robotic framework) can
access the knowledge base without any prior knowledge of the ontologies used
within the KMS. For example, this avoids client applications to deal with OWL
8
https://pythonhosted.org/SuRF/
9
https://github.com/ActiveRDF/ActiveRDF
50 Asprino et al.
and RDF or to interact with a knowledge base by means of SPARQL queries.
Additionally, Lizard embeds an Access Control Management System (ACMS)
that enables the setup of specific access policies in order to guarantee the ac-
cess (either in read or write mode) to specific knowledge areas only to a set of
allowed entities/systems. For example, an application that performs some en-
tertainment activity (e.g. play music) would not be allowed to access knowledge
about the Continuous Geriatric Assessment (CGA). Hence, the ACMS allows
Lizard to deal with some important data management aspects regarding data
access, security and data privacy.
5 Conclusions
In this paper we introduced the MARIO Ontology Network (i.e. MON) and
Lizard that are two components of the knowledge management system adopted
by MARIO robots. These two component enable (i) modular knowledge organ-
isation and (ii) dynamic APIs generation in oder to enable the development of
intelligent abilities in MARIO robots.
Acknowledgements. The research leading to these results has received fund-
ing from the European Union Horizons 2020 the Framework Programme for
Research and Innovation (2014-2020) under grant agreement 643808 Project
MARIO ”Managing active and healthy aging with use of caring service robots”.
References
1. Bisset, D.L.: D1.1 MARIO System Specification for Pilot 1. Tech. rep., MARIO
cosortium (dec 2015)
2. Blomqvist, E., Presutti, V., Daga, E., Gangemi, A.: Experimenting with eXtreme
Design. pp. 120–134. Springer (2010)
3. Gangemi, A., Presutti, V.: Ontology Design Patterns. pp. 221–243. Springer (2009)
4. Lim, G.H., Suh, I.H., Suh, H.: Ontology-based unified robot knowledge for ser-
vice robots in indoor environments. IEEE Transactions on Systems, Man, and
Cybernetics-Part A: Systems and Humans 41(3), 492–509 (2011)
5. Oren, E., Heitmann, B., Decker, S.: ActiveRDF: Embedding Semantic Web data
into object-oriented languages. Web Semantics 6(3), 191–202 (2008)
6. Prestes, E., Carbonera, J.L., Rama Fiorini, S., Jorge, V.A.M., Abel, M., Madhavan,
R., Locoro, A., Goncalves, P., Barreto, M.E., Habib, M., Chibani, A., Gérard, S.,
Amirat, Y., Schlenoff, C.: Towards a core ontology for robotics and automation.
Robotics and Autonomous Systems 61(11), 1193–1204 (2013)
7. Presutti, V., Lodi, G., Nuzzolese, A.G., Gangemi, A., Peroni, S., Asprino, L.: The
role of ontology design patterns in linked data projects. pp. 113–121. Springer (2016)
8. Rockel, S., Neumann, B., Zhang, J., Dubba, K.S.R., Cohn, A.G., Konečnỳ, Š., Man-
souri, M., Pecora, F., Saffiotti, A., Günther, M., et al.: An ontology-based multi-level
robot architecture for learning from experiences. In: Designing Intelligent robots:
Reintegrating AI II. pp. 52–57. AAAI Press (2013)