Ontology-based models in pervasive
computing systems
JUAN YE,LORCAN COYLE,SIMON DOBSON and
PADDY NIXON
Systems Research Group, School of Computer Science and Informatics, UCD Dublin, Ireland;
e-mail: juan.ye@ucd.ie, lorcan.coyle@ucd.ie, simon.dobson@ucd.ie, paddy.nixon@ucd.ie
Abstract
Pervasive computing is by its nature open and extensible, and must integrate the information from
a diverse range of sources. This leads to a problem of information exchange, so sub-systems must
agree on shared representations. Ontologies potentially provide a well-founded mechanism for the
representation and exchange of such structured information. A number of ontologies have been
developed specifically for use in pervasive computing, none of which appears to cover adequately
the space of concerns applicable to application designers. We compare and contrast the most
popular ontologies, evaluating them against the system challenges generally recognized within the
pervasive computing community. We identify a number of deficiencies that must be addressed in
order to apply the ontological techniques successfully to next-generation pervasive systems.
1 Introduction
In 1991, Mark Weiser claimed that ‘the most profound technologies are those that disappear.
They weave themselves into the fabric of everyday life until they are indistinguishable from it’
(Weiser, 1991). His work pioneered the field of ubiquitous or pervasive computing; pervasive com-
puting systems are interactive systems that involve multiple devices, services, and software agents.
They provide appropriate behaviours adapting to the user’s changing tasks and environments
through different interface modalities and devices (Dobson & Nixon, 2004). Context informs
this process by providing a structured, unified view of the world in which the system operates
(Coutaz et al., 2005). The best and most adaptive pervasive computing applications are those
that are most context-aware.
In April 2004, the European Commission’s IST Future and Emerging Technologies group and
the US National Science Foundation jointly supported the Disappearing Computer Strategic
Research Workshop (Skordas et al., 2004). The objectives of this workshop were to consolidate
the research experiences in the domain of pervasive computing and to map out the core and fun-
damental challenges for the next stage of research in the field. The discussions at this workshop
came under many areas, including discovering the fundamental primitives of pervasive computing,
understanding their semantics, and developing corresponding implementations. In the area of
information retrieval and management, they included developing semi-automatic approaches
that allow users, devices, and applications to extract from their environment the necessary infor-
mation to operate. In the area of security, they covered security, privacy, and trust infrastructures
that aimed to maximize user confidence in pervasive computing systems. In the area of human–
computer interaction, the discussions covered the development of hardware infrastructure for
input and output interaction, of software infrastructure for manipulating and controlling inter-
action devices, and of core enabling middleware services. The workshop also covered a discussion
The Knowledge Engineering Review, Vol. 22:4, 315–347.
2007, Cambridge University Press
doi:10.1017/S0269888907001208 Printed in the United Kingdom

on the potential impact of influential, new, and developing technologies on the field. These discus-
sions led to a special issue on ‘The Disappearing Computer’ in the Communications of the ACM
(Streitz & Nixon, 2005), which refined these initial areas into five guiding themes:
Sensing and context In order to develop reactive pervasive computing systems it will be necessary to
capture, process, and exploit the contextual parameters that inform and guide human behaviour.
Privacy, trust, and security Privacy encompasses reasoning about trust and risk involved in the inter-
actions between users and services. Trust controls the amount of information that can be revealed in
an interaction. Risk analysis allows us to evaluate the expected benefit that would motivate users to par-
ticipate in these interactions. Security describes the cryptographic techniques used to secure the commun-
ication channels and required data.
Discovery One of the key requirements for pervasive computing systems is an approach or service cap-
able of assimilating and filtering information from its various inputs (such as sensors, services, applica-
tions, and users). Given some infrastructure to communicate this information, an approach to
matching will be needed, which correlates relevant input events and facts to a particular contextual ser-
vice. This is essential to allow the user and the application to discover the necessary information from
the environment to achieve a defined goal or complete an activity.
Interaction design As computers disappear from pervasive computing environments, novel human–
computer interactions will need to be investigated to deal with the peculiarities of their environments,
including invisible devices, implicit interaction, and real, virtual, and hybrid interactions.
Essential infrastructure It will be necessary to develop tools to maintain and upgrade infrastructure over
its entire life cycle, as well as to allow the infrastructure to communicate failures effectively to its users.
Besides the challenges in the above five themes, we add an additional challenge for pervasive
computing, that of modelling and handling uncertainty. Many data in a pervasive system are
inherently uncertain, since they come directly from real-world sources, which often provide data
that are incorrect, imprecise, conflicting, or incomplete. For this reason, pervasive computing
systems should integrate uncertainty into every decision they make.
Ontologies, as a promising means for knowledge sharing and reuse, have gained recognition in
other fields of computer science, including e-commerce (Obrst et al., 2001; Eckstein et al., 2004),
information integration (Guidetti, 2002; Varzi & Vieu, 2004; Lo´ pez de Vergara et al., 2003), and
the semantic Web (Gil et al., 2005; Sure & Domingue, 2006). Ontologies provide standard and
formal semantics, whose strength in conceptualization is exerted by their normalization and form-
alization. The normalization is reflected in a semantic agreement for the meaning of terms. The
formalization is reflected in formal ontology languages that are used to encode ontologies
(Bachimont et al., 2002). This survey analyzes the existing use of ontologies in pervasive computing
and proposes directions for future work, along the lines of the strategic themes outlined earlier.
The remainder of the paper is arranged as follows. Section 2 introduces the prominent def-
initions of ontologies with emphasis on the nature of ontologies, and describes the standard lan-
guages that are used to build ontologies. It summarizes the advantages of ontologies for computer
science applications, and describes the issues relative to formal ontology development. It also out-
lines a number of criteria under which existing ontologies should be evaluated.
Section 3 introduces several prominent pervasive computing systems that use ontological mod-
elling. We focus our analysis on CoBrA, developed in the University of Maryland (Chen et al.,
2004b), which uses a pervasive ontology called SOUPA to characterize the key concepts of
pervasive computing; Gaia, developed in the University of Illinois at Urbana-Champaign, which
uses ontologies to deal with context awareness, service discovery and matchmaking, and inter-
operation between entities in a pervasive computing environment (Ranganathan et al., 2004b);
GLOSS, developed by four European universities, which employs ontologies for the precise under-
standing of various contexts and services in the smart space (Coutaz et al., 2003); ASC, developed
in the German Aerospace Center, which uses CoOL (Context Ontology Language) to enable con-
text awareness and interoperability (Strang et al., 2003b); and CONON, developed at the National
University of Singapore, which constructs the upper ontologies for general concepts in a pervasive
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