Carlos Lamsfus, Aurkene Alzua-Sorzabal
Competence Research Centre in Tourism, Paseo Mikeletegi, 56 - 201, San Sebastia Spain
David Martin, Zigor Salvador, Alex Usandizaga
Competence Research Centre in Tourism, Paseo Mikeletegi, 56 - 201, San Sebastian, Spain
Keywords: Context modelling and management, Ontology engineering, Networked ontologies, Tourism.
Abstract: A lot of work has been done up to now in the so called context-aware research field on the one hand and on
the ontology research field on the other. Research has been conducted both considering context-awareness
and ontology as clearly distinct research disciplines and also utilizing ontologies as a tool for context
management. However, context-based applications have only been possible at a laboratory environment so
far and they have always worked under very certain, pre-established pre-requisites in a not very stable nor
efficient manner, which actually does not fulfil the nature of Ubiquitous Computing vision. Representation
and use of context plays a crucial role in many modern IT applications. The ability to process contextual
information and perform context-based reasoning is essential not only for mobile and ubiquitous computing
systems, but also for a wide range of tourism applications. This paper presents a novel semantic-based
human-centric approach to the notion of context that represents an attempt to make Contextual Computing
services available to the general public.
Ontologies are now considered (within Computer
Science) as a commodity that can be used for the
development of large number of applications in
different fields such as knowledge management,
eCommerce, intelligent integration of information
and information retrieval (Corcho et al. 07) amongst
Originally, the word Ontology (mind upper case
‘O’) (Guarino et al. 95) comes from philosophy.
From a philosophical point of view, Ontology is the
branch of philosophy that deals with the nature and
organization of reality and things. More recently,
within Computer Science, ontologies (mind lower
case ‘o’) (Guarino et al. 95) aim at capturing domain
knowledge in a generic way and provide a
commonly agreed understanding of a domain, which
may be re-used across applications (Chandrasekaran
et al. 99) (Corcho et al. 01).
Ontologies first started to be used back in 1991
within the context of the DARPA (Defence
Advanced Research Projects Agency) Knowledge
Sharing Effort (Neches et al. 91) (Corcho et al. 07).
The origin of that work was in the efforts the
Artificial Intelligence Community was doing at the
time to find new ways to share knowledge. In fact,
the objective of that project was to explore new
ways to construct knowledge-based systems so that
knowledge bases upon which the systems were
based did not have to be built from scratch, but by
assembling re-usable components, saving this way
time and money.
In more recent years, ontologies have extensively
been used in Pervasive Computing environments as
well as a tool for developing and realising Context
Aware systems (Strang 03) (Chen et al. 04b) (Gu et
al. 04) (Ay 07). There have even been authors that
claim that ontologies are key to the realisation of
Context-Awareness (Chen et al. 03). Since Mark
Weiser enunciated his vision of a new computing
paradigm called Ubiquitous Computing (Weiser 91)
a lot of effort has been invested and research
conducted into investigating the notion of context
and context-aware systems (G. Chen et al. 01)
Lamsfus C., Alzua-Sorzabal A., Martin D., Salvador Z. and Usandizaga A. (2009).
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 424-434
DOI: 10.5220/0002300704240434
(Vazquez 07). However, these systems have not yet
been made available to the general public.
We believe this is due to several reasons, including
the lack of adequate infrastructure to develop such
applications (Gu 04) (Dey 01), the lack of a common
understanding of the notion of context (Ay 07) and
the consequent lack of an agreed context model, just
to mention a few. Additionally, all of the followed
approaches have an eminently techno-centric
conception of context, as they all focus on the
system rather than on the individual. Moreover,
context has never been studied as such, but as a tool
for other research fields such as human-computer
interaction (Dey 00), software agents (Chen 04) or
Distributed Systems (Strang 04) for example, where
the authors use contextual information to enhance
their systems’ functionalities, but not for the sake of
studying context itself. In addition, most context-
aware applications require populating an area of
interest with sensors and additional devices that are
utilized to gather contextual information.
The lack of an integrated and operative definition of
context as well as a sound context-management
model, together with the limiting factor the usage of
sensors to gather data represent are some of the
reasons why context-awareness is yet limited to
certain academic circles and laboratory work, posing
serious barriers to the widespread adoption of the
context-aware vision.
Context-based applications are the opportunity and
the future in the Travel and Tourism Industry
(Bernardos et al. 07). According to reports by the
WTTC (WTTC) and the UNWTO (WTO) people
move more and more frequently (Hall, 2005) and
they demand online services anytime, anywhere.
The context of a tourist is essential to retrieve
relevant pieces of information at a given moment of
time as it enables dynamic, personalized delivery of
services and information to visitors, significantly
enhancing their mobility and tourism experiences.
The unexploited potential of Contextual Computing
for all kinds of mobility-related scenarios is huge,
and tourism and tourists can greatly benefit from a
rigorous and inherently enabling approach to context
This paper presents a piece of ongoing research
work that tackles with the barriers we have
encountered that are stopping Contextual Computing
applications from becoming universal. It considers
the context of a visitor from a totally different point
of view to traditional approaches: we place the
visitor at the very centre of the problem and we
model his context and the domains which are
relevant to that visitor with the use of ontologies.
The rest of the paper is divided as follows: Section 2
summarizes the related work found in the literature
with regard to context and context-awareness,
ontologies and ontologies as tools to model and
manage contextual information. We show in Section
3 the motivation underlying this research work as
well as some definitions. Section 4 describes the
context ontology that we put forward in this paper as
well as the development methodology followed to
build the ontology. Finally, Section 5 draws some
conclusions and remarks some future research lines.
The history of context aware systems started when
Want and colleagues (Want et al. 92) introduced
their Active Badge Location System. Baldauf and
colleagues (Baldauf et al. 07) refer to this
application to be one of the first context-aware
applications. This first notion of context in computer
science was solely restricted to the location of
people in an office environment. However, the
location of an individual is only one of a large
number of variables that may be used to define
context. This definition turns out to be too broad in
order to build a contextual-information based
Schilit and Theimer (Schilit et al. 94) are the
authors who first used the term context-aware. In
their work the authors state that humans live in a
mobile and ever changing environment in which
they interact with a number of different devices.
These authors show a broader notion of context as
they assume that context is location as well as other
important aspects such as who you are with and
what resources are nearby. Still this definition is too
wide and somewhat vague. Many concepts ought to
be clarified within the definition, e.g. what is that
defines who somebody is, or what the coverage of
the notion of nearby is in terms of the space scale,
One of the most popular definitions of context
has been given by Dey and Abowd (Baldauf et
al.07). Their approach to the notion of context is
through Human-Computer Interaction abstractions.
These authors (Dey et al. 00a) (Dey 01) refer to
context as: “any information that can be used to
characterize the situation of an entity, i.e. a person, a
place, an object, etc., that are considered to be
relevant to the interaction between a user and an
application, including the user and the application
themselves” (Dey, 00. Providing Architectural
Support for Building Context Aware Applications,
p. 4. Ph.D Dissertation. Georgia Institute of
One important research question with regard to
context is about the way context ought to be
managed and used. Unsurprisingly, the lack of a
unified and widely accepted answer to this question
(as well as a common and sufficiently established
understanding on the notion of context itself) has
made each researcher focus on the specific context-
related functionality they need to apply in their
research fields of interest, rather than on context
These first authors working on the realm of
context-awareness did not use ontologies to model
and manage their idea of context. Ontologies at the
beginning of the 90s were hardly known and by that
time their real potential and functionality had still
not been recognised. So, ontologies were simply not
even considered as an option for context
However, in parallel to research conducted in
context-awareness, the Artificial Intelligence (AI)
community had recognized that capturing
knowledge is the key to building large and powerful
AI systems and applications (Neches et al. 91). Of
course, one of the most complex problems
researchers had to face was the need to represent
captured knowledge so that they could make some
sort of meaningful understanding about it and set the
rules under which knowledge could and ought to be
shared and re-used across (computing) applications.
The problem of Knowledge Representation and
Sharing has been widely studied by authors like
Allen Newell (Newell 80), Nicola Guarino (Guarino
95), Gruber (Gruber 93) (Gruber 94), Musen (Musen
92) and many others.
Some of the work in AI at the beginning of the
90s explored the way to use formal ontologies as a
way to specify content-specific agreements for
sharing and re-using knowledge among software
entities (Gruber 94). This way, declarative
knowledge, problem-solving techniques and
reasoning services could all be shared among
systems. In fact, this same conception and
philosophy is precisely what underlies within the
(ontology or semantic-based) context model that we
put forward in this paper: provide the way in which
we can share at least part of the (individual’s)
context with other kinds of context (domains of
reality) and thus provide effective context based
information services in tourism in an effective
anytime, anywhere manner.
Around 2000 research scientists on the realm of
context-awareness still did not have a clear idea
about the notion of context and still did not have
either an agreed context definition or model.
Moreover, no research work had properly analysed
the generic use of context information in Computer
Science. However, the work that had been done on
ontologies during the 90s elucidated that they could
support knowledge re-use, integration and sharing
across applications and therefore several authors
converged upon a fact: context information and
context models could be handled using semantic
technologies (Gu 04) (Chen 04) (Strang 03), as a
first step towards standardization or an attempt to
making these systems universal.
In particular, they have used ontologies to
represent their context models and manage
contextual information in an efficient and organized
manner. Regarding the nature of context, these
authors simply take Dey’s definition and apply
semantic technologies to build information systems
on top of it. These authors claim that ontologies may
provide a shared context model. In addition to that,
ontologies can be further used for reasoning (infer
high level implicit context from low level explicit
context, for example) as well as to detect data
consistency and duplicity.
The Literature Review reveals that most research
in the field of context and contextual computing is
not focused on context itself, but on particular uses
of context: authors consider it as a simple set of
variables which are relevant to their application field
of interest and tend to contextualize the environment
of the individual, not the individual within the
environment. These approaches turn out to be
extremely restrictive and miss the potential
contributions in the field of contextual computing.
This is also one of the reasons why there is not
an agreed definition of context, because the
objective of the piece of research has not been
context, it has been something else. In fact, due to
their emphasis in contextualizing the environment,
most of the existing research work revolves around
the existence of a network of sensors in the
environment and other specifically deployed devices
and SW solutions, missing what we believe to be the
greatest and most meaningful contextual information
source: the Internet. The use of sensors poses in our
opinion one of the greatest barriers that is preventing
Contextual Computing applications from becoming
universal. Still in tourism, location based and
context-based applications have to get off the ground
(Buhalis et al. 08).
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
3.1 Motivation
According to figures provided by the United Nations
World Tourism Organization (UNWTO) and the
World Travel and Tourism Council (WTC) the
Travel and Tourism industry is one of the largest and
most important industries in the entire world.
Around 90% of the visitors around the World
carry a mobile electronic device with them at all
times and require in some way or another to be
connected to sources of information, such as the
Internet. This kind of devices, e.g. mobile
telephones, PDAs and the like are fast evolving into
miniature computers. In fact, devices such as
Apple’s iPhone, Google’s Android, the new Nokia E
series, etc. have impressive computing capabilities
and are fast blurring the vague line between laptop
computers and mobile devices. These new
generation mobile devices are regarded as the main
access to the Internet in the future. In addition,
connectivity technologies, such as 3G, UMTS,
HSDPA, Wi-Fi, etc. allow visitors to be connected
almost anytime and anywhere to information
Given the enormous amount of information that
exists in the Internet, to access the right piece of
information at a particular moment could be a real
challenge to say the least. In this sense, the role of
the visitor’s context is crucial as it can be used as a
kind of filter to access a particular piece of
information that is relevant to support and enhance
the visitor’s mobility.
3.2 Definitions
3.2.1 Contextual Computing
Firstly, we would like to make a remark on how we
refer to the discipline under discussion within the
Most of the literature refers to context-aware
systems or applications to denominate systems that
make use of information that originates within the
context in which they run. These applications have
been programmed to automatically react (in various
ways) to changes that occur in their environment
without explicit human intervention. However, we
consider that this way of functioning does not make
these systems either aware of their context or
intelligent as we argue that awareness is an
eminently human ability and as such, computers
cannot be aware of anything. These (context-aware)
systems have been enabled to detect, gather, manage
and process contextual information under certain
rules or system governing regulations. They just
process information. This is the reason why we
would rather talk about Contextual Computing,
rather than talking about context-awareness. As our
domain of application is the Travel and Tourism
industry, then we refer to Contextual Computing in
So, for us, Contextual Computing is the
scientific discipline that studies and observes the
context of an individual and pursues to generate
knowledge out of the observation in terms of how to
model an individual’s context and how to manage
information originated in that context. It also
explores how that information can be processed in a
way that it is useful for the individual.
3.2.2 Definition of Context
The concept of “visitor” is defined by the UNWTO
as “a traveller taking a trip to a main destination
outside his/her usual environment, for less than one
year for any main purpose (business, leisure, or
other personal purpose) other than to be employed
by a resident entity in the country or place visited.
These trips are taken by visitors qualify as tourism
trips. Tourism refers to the activity of all visitors”,
(UNWTO International Recommendation for
Tourism Statistics, 2008, p. 10).
We propose to study the context of the visitor as
such context, i.e. not as an auxiliary variable of
something else. We focus on the domain of the
application and attempt to generate knowledge out
of the questions originated from that observation.
Under these circumstances: what is that defines the
context of an individual? What is the minimum
amount of information that we need to define that
individual’s context? Where is it (the information)
and how can we obtain contextual information? How
can we translate that context into a computing model
so that it can provide the visitor with relevant
information to enhance his mobility?
In addition, we propose to gather contextual
information from alternative sources of information
regardless of the existence of sensor networks. We
propose to use the Internet as the main contextual
information source that can be complemented with
mobile device incorporated sensors (e.g. GPS). This
way we would avoid having to populate a particular
area of interest with sensors. The objective is not to
contextualize a particular area, but to contextualize a
particular individual at a particular location at a
particular moment of time by the use of web-based
information. This way, we expect to set the
conceptual foundations for meaningful contributions
in the fields of contextual computing and tourism.
We believe this is one first step that could contribute
to universalize Contextual Computing systems and
making them available to the general public.
Our model of context focuses on the human
being itself, it does not consider an application, a
service or the context information that may be
relevant for the application to run more efficiently. It
considers the information that is relevant to
characterize the situation of a tourist and that can be
beneficially used to enhance, improve and assist
visitors while en route. In this sense we would like
to propose a definition of the notion of context of
our own which is based upon the definition put
forward by Dey (Dey 00): “Context is any relevant
information that characterizes the situation of a
visitor. A visitor is a traveller taking a trip outside
his/her usual environment and her situation is
specified by data concerning a) the individual itself,
b) the individual's environment (and surroundings)
and c) the individual's objective at a particular
moment of time. This information can be of use for a
computing-application in order to support the
visitor's mobility”.
3.2.3 Justification of the Context Ontology
Strang and colleagues (Strang et al. 03) studied
various kinds of context models. They analyzed
them according to requirements they had set
themselves for Ubiquitous Computing systems and
they found as a result that ontologies clearly fulfil all
requirements and are one of the most adequate (if
not the most) tool to model contextual information.
In fact, one of the biggest advantages of ontologies
is their flexibility and capability to model a domain
and, hence, conceptualize the portion of reality to
which such a domain refers (Toro et al. 08).
Strictly from a pure theoretical point of view,
based upon the original philosophical conception of
the notion of Ontology, according to Aristotle
(Klimovski 05) the way in which science is
communicated is based on language, i.e. semantics.
So, Semantics in Aristotelian philosophy represent
the relationship there is between the reality of things
in the world (Aristotle’s concept of Ontology) and
the idea (model) that we form of them in our minds.
A context model is a formal representation of the
individual’s context. The model can be constructed
through a set of concepts, properties and relations,
i.e. ontologies. In addition to that, a context model is
an abstraction of an individual’s context in reality at
a given moment of time. Therefore, as well as we
use semantics (i.e. natural language) to explicitly
express our idea about the world, ontology
semantics (i.e. ontology development languages) can
convey the reality of the model to a computing
This parallelism between the notion of Ontology
in Philosophy and the notion of ontology within
Computer Science as a tool to model context
theoretically and conceptually grounds the use of
ontologies to model context. The abstraction of
reality (i.e. context of an individual at a given
moment of time, Ontology) in a computing system
can be represented through ontologies. The
relationship that exists between the model (mental
abstraction) and the reality is expressed through
semantics (language) (Klimovski 05) as well as the
computing model of context (ontology) can be
expressed through the (ontology’s) semantics, i.e.
ontology languages.
Besides, ontologies have proved to be good
intermediation tools in information integration. This
is crucial in our vision of context: under this vision
firstly, we contextualize the visitor and secondly, we
divide the world in different domains, e.g. a city,
museum, restaurant, etc. As both the visitor’s
context and the domains will be modelled by the use
of ontologies it will be very simple to attach the
different ontologies and have them work together,
allowing interoperability and interaction among
context models. In addition, ontologies can also
provide reasoning functionalities that are valid for
the context model.
Finally, it would be very convenient to be able to
detect or reason on the activity the visitor is
undertaking at the particular moment of time. This is
one of the reasons why we shall use ontologies to
model visitors’ context.
3.2.4 System Architecture
Building contextual computing systems involves
several challenges, such as gathering, modelling,
storing, and managing contextual information. These
challenges justify the need for an architectural
support to provide an efficient infrastructure for
building this kind of systems.
The architecture is based on a layered
distribution in order to separate low-level tasks
(discovering and gathering context, storing) from
high level tasks (managing context, querying). It
consists of the following interconnected components
distributed on different layers.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
Figure 1: Proposed system architecture.
Context providers. They are used to acquire
context data from heterogeneous sources.
They can acquire context information from
web sources, e.g. weather web services, or
from the visitor’s mobile device, e.g.
profile, location. This is one of the
novelties within our system: we are not
limiting the use of our system to a
particular predetermined sensor-populated
location, but we can use it in every single
place where there is telephone network
coverage that enables access to information
Context manager. It gets the information
from all the context providers and it is
responsible for gathering context,
transforming context data into the
ontological model and merging all data into
de Knowledge Base. It offers a centralized
way to access context data sources;
Knowledge base. It stores all the statements
about tourist’s context by the use of
Context history. A historical database of
past context variables’ values is stored here.
This can be useful to predict future visitor
situations by the use of the Context History
Exploitation Engine or to reason over
current values of context variables, e.g. the
coordinates given by the mobile device
GPS incorporated sensors correspond to
Athens and they do not exist in the Context
History database, therefore, the visitor is in
Athens for the first time;
Reasoning engine. It is used to obtain high
level context (situations) based on defined
rules or the semantics of information that
has been gathered and stored in the
Knowledge Base;
Query engine. It allows queries about
context information, as location,
temperature or higher level context;
Privacy, Trust and Security Control: Given
that contextual information may have very
sensible personal information, we need to
consider a module within the architecture
that actually takes this fact into
consideration and that allows the visitor
decide to what extent she wants to share
personal information either with others or
with the system;
Access manager. It manages the interaction
between the platform and the application
layer. This interaction can be in a
request/response manner or in subscription
basis, where the platform sends context
information according to defined events
(context changes, time intervals);
Application layer. Application that can
interact with the platform in order to adapt
its behaviour to the user’s context.
We shall determine which the constituents of context
are based upon the definition of the notion of context
we have put forward earlier in this paper, the
architecture that we have presented in the previous
section and on the final objective of the Contextual
Computing Application that we are designing. These
constituents of context will end up being one
ontology each within the ContOlogy network of
ontologies and will define the relationships among
However, we need to distinguish several issues
at this point. We take Davenport’s (Davenport et al.
01) definition of data and information, whereby:
“Data are the values of parameters (definition) and
variables that result from some kind of work” and
“Information is communicated data, i.e. there is a
transmission channel as follows: there are agents in
form of sender and receiver, there is a channel that
is being used to communicate and, finally there is an
encoding and decoding process”.
Within the definition of context, three different
categories of data can be found:
Category a: refers to information about the
visitors themselves;
Category b: refers to information about the
individual’s environment;
Category c: refers to the intentions and
objectives of the individual, i.e. data about
the next future that compared to the
information that describes the context of the
visitor at a given moment of time could
define what the relevant information the
visitor needs at that particular moment of
This information is hardly transferable to a
computing model that represents the context of a
visitor at a given moment of time. We need to define
variables that represent data belonging to each
category. These variables will then be used in the
computing model and this model will behave
according to the values of these variables.
In order to define the variables, we shall use the
5W (Dey and Abowd 00) (Who, What, When,
Where and Why) and one H (How?) as basic
information gathering system within the context of
the visitor:
Information about category a), i.e. information
about visitors themselves, i.e. information
about the visitor as a human being and some
characteristics inherent to the visitor as such.
This information can be obtained by
answering the who, what and how questions:
Who is the visitor? The “who” can be
defined by the visitor’s id, her mood,
her profile and her role;
What is that visitor doing? Task, activity;
How is the visitor proceeding? Device;
Information about category b), i.e. information
about the visitor’s environment, i.e. the set of
relevant elements or entities that happen to be
at the same location as the visitor. Explicit
entities, such as infrastructure –network-, can
also be taken into account within this
category. This information can be obtained
by answering the where, when and how
Where is the visitor? Location
(coordinates, reasoning street, city,
country potentially obtainable), weather
conditions (temperature, sunny, etc.);
When is the visitor at that location? Time,
date, etc.
How? Device (type of device), network
and connectivity information;
Information about the visitor’s objective can be
obtained by answering the why question:
Why is the visitor at that location?
What is going to do next?
What are his needs?
We aim to re-use as much existing contextual
models that are supported by a significant numbers
of practitioners as possible. Therefore, we will not
develop a single ontology but a network of
ontologies, i.e. a collection of ontologies that are
related among them by properties (Haase et al.06).
Despite the fact that there are already a
considerable number of context modelling
ontologies, they are still in an early preliminary
experimental phase. As it is the case with most of
the work that has been done up to now, they have
been defined for different specific uses and cover
different domains. Therefore they have been
basically designed for specific purposes which make
them hardly re-usable. Hence, no consensual model
exists that can broadly be re-used for modelling
context in applications. Furthermore, even if there
have been plenty of efforts for developing context
ontologies, only few of them are available to be
studied in detail and reused; these are the
CoDAMoS (Preuveneers, et al. 2004), GUMO
(Heckmann et al. 2005) and SOUPA/COBRA-ONT
ontologies (Chen et al. 05), CC/PP (W3Ca 2004)
and Delivery Context Ontology (W3Cb 2008).
4.1 Ontology Building Methodology
Different methodologies to build ontologies have
been reported in the literature (Gruber 94)
(Grüninger et al. 95) (Uschold et al. 96) (Bernaras et
al. 96) (Noy et al. 01) (Corcho et al. 01) (Gómez-
Pérez et al. 03). However, due to the fact that we are
planning to build a network of ontologies rather than
a single ontology from scratch, we shall use the
NeOn methodology for developing ontology
networks (Suárez-Figueroa et al. 2008).
Both the literature and experience have shown
that the ontology building process is iterative.
Therefore, the ContOlogy context ontology network
will be implemented in three consecutive iterations,
each of them providing a working prototype of the
ontology network suitable for validation of the
model. This approach is different from others in
terms that it does not have a double ontology
conception of context, i.e. a core ontology and other
domain ontologies. Rather, it focuses on the
different constituents of context (derived from the
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
definition of the notion of context, architecture of
the system and main objective of the system) and
develops an ontology for each of the constituents.
This adds modularity and flexibility to the ontology
model that we are proposing in this paper. Domain
ontologies that represent specific parts of the world
could be aligned to ContOlogy according to the
particular context of a visitor at a given moment of
At the moment of writing this paper, we have
completed the first iteration of the ontology network
development. In this iteration our goal was to obtain
a first set of ontology requirements and a first
prototype of the ontology network that could be used
in early stages of the project. The results of the
evaluation of this first iteration of the context
networked ontology (ContOlogy) will be used,
together with other information sources (e.g.,
empirical data) in future iterations. As it has been
argued in the literature, the insufficient involvement
of final users (visitors in this case) in the
construction of ontologies is a significant cause for
the current shortage of and the unsatisfying coverage
found in domain ontologies (van Damme et al. 2007)
The following represent some of the activities
we have carried out up to now within the first
iteration of the ontology development process:
ontology specification, scheduling, re-use of
ontological resources and ontology implementation.
The following table presents the result of this work.
Table 1: Result of first iteration of ContOlogy.
Ontology Definition
Visitor (WTO, 2008) Characteristics of the human
being in mobility
Profile Information that describes
the visitor’s preferences
Motivation (WTO, 2008) Classification based on
main purpose of mobility
Device Physical object the visitor
carries with him
Network Infrastructure to connect
devices and convey
Intention An aim, plan or purpose
Location Coordinates that define
where a visitor is at a given
moment of time
Time Physical dimension that
measures spam between
Weather Meteorology conditions at
the given location
Some of the ontologies (Activity and task for
example) have not yet been implemented at this
stage of the ontology building process. They shall be
tackled in the following iteration. Moreover, the
ontologies within the network are related to each
other via typical properties, such as is_a, has, etc.
4.2 Use Case Validation
The tourism domain is widely considered to be one
of the emerging industrial sectors where mobile
services are highly demanded. In fact, in 2015 there
will be more than 3 billion travellers around the
world and they will demand more ubiquitous
services, specific to the situation of each individual,
as well as to their personal preferences under
specific circumstances. Surveys reveal that over
90% of travellers carry a mobile device with them.
Thus, tourism turns out to be a very adequate
application domain for contextual computing
The following use case scenario has been
designed in order to validate the proposed context
definition and context model as well as the proposed
architecture to support contextual computing tourism
services. Such services can be driven to support the
traveller’s mobility while the visitor be at a
particular unusual destination.
Let us consider a particular individual that has
arrived in a city with his wife. That information can
be obtained by the location of the mobile devices of
both individuals: both of them are located together
in this new city to visit. Also, based on the context
history it is known that the travellers are visiting the
city for the first time. The mobile phones send their
location coordinates as well as their owner’s identity
to the Context Provider. The Context Manager
inserts this information on the Knowledge Base and
the system concludes through reasoning that the
couple is in that city for the first time.
The system then explores on their profile. Given
that the individuals are not familiar with the city, the
different possible places to visit are selected by the
service based on the user’s combined preferences
(topics that the users were interested in previous
similar situations, i.e., while visiting new cities in
the past). Finally, a first place to visit is displayed on
the mobile phone screen.
The system has determined through reasoning
that the travellers arrived in the city by train,
therefore the previous information is shown in the
screen along with the public transport options
available. While on the bus, the travellers do not
know in which bus stop they need to get off the bus.
Given their current location, the location of the point
interest of their choice and the closest bus stop to the
point of interest, the system warns them about the
most convenient bus stop. The service will keep the
travellers informed about such topics, specific to
route events while visiting the city.
The individuals may also get information about
nearby museums compatible with the user’s
preferences or hobbies. Specifically, the users may
get special last-minute offers, based on the fact that
they can be very close to the museum. For instance,
a museum that might be interesting for the users is
displayed on the mobile phone. In ten minutes time,
a visit group is available with two free places to
complete the group. Given that the museum is
interested in completing the visit group, the users
subscribed to the contextual recommendation service
get special last-minute discounts if they are close to
the museum. They could also get indications on how
to reach the museum.
We discuss in this paper a different approach to the
notion of context and context-awareness to that
proposed so far in the literature that is called
Contextual Computing. In particular, we focus on
Contextual Computing Services in tourism, although
the most general aspects of our contribution are
relevant to all of the so called context-aware
scientific research discipline.
We have thoroughly analyzed most of the
relevant existing literature and we have concluded
that (i) neither does consensus exist on a definition
for the notion of context nor do existing ones suite
the tourism domain, (ii) a sufficiently agreed model
of context and method for contextual information
management does not exist, (iii) the existing works
reveal the need of a scientific approach to the study
of context on its own and (iv) the use of sensors to
gather contextual information poses a serious barrier
as pre-requisite for making Contextual Computing
systems universal.
The specific contributions of this piece of
(ongoing) research work tackle with these problems
and propose different alternatives.
Firstly, we have proposed a new definition of
context aiming at integrating and making the notion
of context more operative. This new definition of
context is human-centred and contextualizes the
individual at a given location. It observes the nature
of human mobility and opens new chances to study
complex scenarios.
Secondly, this approach does not require the use
of sensors to capture contextual information in
addition to the ones that are already present in the
mobile device. We argue that the individual may be
contextualized according to certain existing
parameters and Web based information sources,
instead of contextualizing a system or a particular
environment. As a consequence, the amount of
imposed preconditions with regard to existing
research approaches is greatly reduced.
This approach makes the application
independent from the need to have a sensor
populated area in a location of interest. In addition, it
also avoids the great amount of complex work that
had to be carried out under the existing approaches
to make context-aware applications run. This is one
first step to make Contextual Computing
applications available to everyday users on the one
hand and to universalize them on the other hand.
Thirdly, by using a network of ontologies to
model context, we are providing a framework of
interoperability for other kinds of systems, as
ontologies have shown to be an appropriate tool for
data exchange and integration. Besides, ontologies
provide reasoning capabilities which are particularly
interesting for data inference, consistency checking
and detection of data duplicity.
In contrast to the frequently used double
ontology approach to model context (one core
context ontology and several domain specific
ontologies) the network of ontologies that we
propose allows to easily align domain specific
ontologies to the network as one more constituent of
the context of a visitor at a given moment of time.
The model we put forward however presents a
number of limitations. As we are proposing not to
use conventional sensors, the applications’
contextual information has to rely on Web based
information, i.e. we need to rely on the fact that data
is accurate and that it is being continuously updated.
Furthermore, there are some kinds of data that
cannot be obtained anyways, e.g. noise level,
lightning level, etc. and therefore context
information is not as rich as it could by the use of
these kinds of sensors. We argue that this
information however is not strictly relevant for a
tourism application and furthermore, the fact of not
having sensors makes it easier to make Contextual
Computing applications universal.
Still there are a lot of open questions. Firstly,
further research is needed on connectivity
technologies. The existing ones provide mobile
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
internet access to a reasonable cost provided we are
not under a roaming service, which considerably
raises the connection price. Wi-Fi, RFID, Bluetooth
and other connectivity technologies could help on
the way. More research is necessary as well on
middleware technologies and platforms to find out to
what extent they can support Contextual Computing
applications efficiency in a domain-divided world. It
is essential to understand how the Future Internet is
going to impact on Context models, even more
considering that the presented new paradigm does
not consider to use conventional sensors to gather
contextual information.
Real visitors shall be involved in an
experimental phase of the ontology development.
This will allow to find out more about intentions and
motivations of a visitor en route in order to include
these into the network of ontologies. This is
something that has not yet been considered in other
context ontologies and as it has previously been
stated, the participation of real users may improve
the usefulness of the final ontology.
The authors would like to thank the Basque
Government for having partially funded the
imFUTOURnet project within the Etortek strategic
research programme and the research undertaken for
the purpose of this paper.
Ay, F., 2007. Context Modelling and Reasoning using
Baldauf, M., 2007. A Survey on Context-aware systems.
International Journal Ad-Hoc and Ubiquitous
Computing, 2, pp. 263 – 277.
Bernaras, A., Laresgoiti, I., Correa, J., 1996. Building and
reusing ontologies for Electrical Networks
applications. In Proceedings of the 12
Conference on Artificial Intelligence.
Bernardos, A. B., 2007. Servicios y aplicaciones en
movilidad para el sector turístico. CITIC 2007.
Buhalis, D., Pistidda, L., 2008. The impact of WiMAX on
Tourist Destinations. In Proceedings of the ENTER
2008 Conference. Innsbruck, Austria, pp. 383 – 394.
Chandrasekaran, B., Josephson, J. R. and Benjamins, R.,
1999.What are ontologies and why do we need them?
In IEEE Intelligent Systems, pp. 22 – 26.
Chen, G., Kotz, D., 2000. A survey of context-aware
mobile computing research. Tech-Report TR2000 –
381. Darmouth College.
Chen, H. L., 2004. An Intelligent Broker Architecture for
Pervasive Context-Aware Systems. Department of
Computer Science and Electrical Engineering,
University of Baltimore, PhD Thesis.
Chen, H., Finin, T., Joshi, A., 2003. An ontology for
Context-Aware Pervasive Computing Environments.
Chen, H., Finin, T., Joshi, A., 2004. A Context Broker for
Building Smart Meeting Rooms.
Chen, H., Perich, F. Finin, T., Joshi, A., 2004. SOUPA:
Standard Ontology for Ubiquitous and Pervasive
Chen, H., Finin, T. and Josh, A. 2005. The SOUPA
Ontology for Pervasive Computing. In Ontologies for
Agents: Theory and Experiences, pp. 233-258.
Corcho O., Fernández-López, M., Gómez-Pérez, A., 2007.
Ontological Engineering: what are ontologies and how
can we build them? Chapter III, p. 44 – 70.
Corcho, O., Fernández-López, M., Gómez-Pérez, A.,
2001. OntoWeb: Technical Roadmap. Universidad
Politécnica de Madrid.
Damme, C. v., Hepp, M., Siorpaes, K., 2007.
FolksOntology: An Integrated Approach for Turning
Folsonomies into Ontologies. In Proceedings of the
ESWC Workshop “Bridging the Gap between
Semantic Web and Web 2.0”.
Davenport, T. H., Prusak, L., 1998. Working Knowledge:
how organizations manage what they know. Ed.
Harvard Business School Press.
Dey, A. K., 2000. Providing Architectural Support for
Building Context-Aware Applications. Georgia
Institute of Technology, Ph.D. Thesis.
Dey, A. K., 2001. The Context Toolkit. A Toolkit for
Context Aware applications.
Dey, A.K., Abowd, G.D., 2000. Towards a better
understanding of context and context awareness.
Proceedings of the workshop on the What, Who,
Where, When and how of Context Awareness, ACM
Press, New York. 2000.
Gómez-Pérez, A., Fernández-López, M., Corcho, O., 2003
Ontological Engineering. ISBN 1-85233-551-3.
Springer Verlag.
Gruber, T. R., 1993. A transaction approach to portable
ontology specifications. Knowledge Acquisition, Vol.
5, pp. 199 – 220.
Gruber, T. R., 1994. Toward Principles for the Design of
Ontologies Used for Knowledge Sharing. In Formal
Ontology in Conceptual Analysis and Knowledge
Representation. Kluwer Academic Publishers. 1994
Gu, T., Pung, H. K., Zhang, D. Q., 2004b. A service
oriented middleware for building context-aware
services. Journal of Network and Computer
Applications, pp. 1 – 18.
Gu, T., Wang, X. H., Hung K. P., Zhang, D. Q., 2004: An
Ontology-based Context Model in Intelligent
Environments. In Proceedings of the Communication
Networks and Distributed Systems Modelling and
Simulation Conference.
Guarino, N., 1995. Formal ontology, conceptual analysis
and knowledge representation. International Journal
of Human and Computer Studies, p. 625 – 640.
Grüninger, M., Fox, M., 1995. Methodology for the
Design and Evaluation of ontologies.
Haase, P., Rudolph, S., Wang, Y., Brockmans, S., Palma,
R., Euzenat, J. and d'Aquin, M., 2006. NeOn
Deliverable D1.1.1 Networked Ontology Model.
Hall, M., 2005. Tourism: rethinking the social science of
Mobility. Ed. Pearson Education Limited.
Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M.
and von Wilamowitz-Moellendorff, M., 2005. Gumo –
The General User Model Ontology. In User Modeling,
Klimovski, A., 2005. Las desventuras del conocimiento
científico. Ed. A Z. ISBN 950-534-275-6.
Musen, M. A., 1992. Dimensions of Knowledge Sharing
and Reuse. Computers and Biomedical research, pp.
435 – 467.
Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.;
Senator, T. & Swartout, W., 1991. Enabling
Technology for Knowledge Sharing. AI Magazine
Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.;
Senator, T. & Swartout, W., 1991: Enabling
Technology for Knowledge Sharing. AI Magazine.
Newell, A., 1980. The Knowledge Level. AI Magazine.
Noy, N., F., McGuinness, D. L., 2001. Ontology
Deveopment 101: A guide to creating your first
ontology. Stanford University.
Preuveneers, D., Van den Bergh, J., Wagelaar, D.,
Georges, A., Rigole, P., Clerckx, T., Berbers, Y.,
Connix, K., Jonckers, V. and de Bosschere, K., 2004.
Towards an Extensible Context Ontology for Ambient
Schilit, B. N.; Adams, N. W. R., Roy, W., 1994. Context-
Aware Computing Applications.
Strang, T., 2003. Service-Interoperabilität in Ubiquitous
Computing Umgebungen. Ph.D. Thesis, TU-München.
Strang, T., Linnhoff-Popien, C., 2004. A Context-
Modelling survey. First International Workshop on
Advanced Context Modelling, Reasoning and
Management. UbiComp.
Suárez-Figueroa, M. C., Aguado de Cea, G., Buil, C.,
Dellschaft, K., Fernández-López, M., García, A.,
Gómez-Pérez, A., Herrero, G., Montiel-Ponsoda, E.,
Sabou, M., Villazón-Terrazas, B. and Yufei, Z., 2008.
NeOn D5.4.1: NeOn Methodology for Building
Contextualized Ontology Networks. NeOn project.
Toro, C. A., Sanín, C., Szczerbicki, E., Posada, J., 2008.
Reflexive Ontologies: Enhancing Ontologies with
Self-Contained Queries. Cybernetics and Systems,
39:2, pp. 171 – 189.
United Nations World Tourism Organization, UNWTO.
Uschold, M., Grüninger, M.,1996. Ontologies: Principles,
Methods and Applications. Knowledge Engineering
Review, vol. 11, pp. 93 – 155.
Vázquez, I., 2007. A behavioural model for Context-
Aware Semantic Devices. Ph.D Dissertation.
Universidad de Deusto.
Want, R., Hpper, A., Falcao, V., Gibbons, J., 1992. The
Active Badge Location System. ACM Transactions on
Information Systems, pp. 91 – 102
Weiser, M., 1999. The Computer of the 21st Century.
SIGMOBILE, Mobile Computing and Communications
Review, 3(3):3-11.
World Travel and Tourism Council.
World Travel and Tourism Council, 2008. International
Recommendations for Tourism Statistics.
W3C, World Wide Web Consortium, 2004.
Composite/Capability/Preference Profiles (CC/PP):
Structures and Vocabularies 1.0. W3C
Recommendation 15 January 2004
W3C, World Wide Web Consortium, 2008. Delievery
Context ontology. W3C Working Draft 15 April 2008.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development