HUMAN-CENTRIC ONTOLOGY-BASED CONT

EXT

MODELLING IN TOURISM

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.

1 INTRODUCTION

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

others.

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)

424

Lamsfus C., Alzua-Sorzabal A., Martin D., Salvador Z. and Usandizaga A. (2009).

HUMAN-CENTRIC ONTOLOGY-BASED CONTEXT MODELLING IN TOURISM.

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 424-434

DOI: 10.5220/0002300704240434

 SciTePress

(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

information.

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.

2 LITERATURE REVIEW

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

system.

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,

etc.

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,

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425

p. 4. Ph.D Dissertation. Georgia Institute of

Technology).

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

itself.

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

management.

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).

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3 MOTIVATION AND

DEFINITIONS

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

sources.

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

paper.

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

tourism.

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

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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

entity.

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.

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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

sources;

• 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

ontologies;

• 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.

4 DEFINITION OF THE

CONTEXT ONTOLOGY AND

ITS CONSTITUENTS:

CONTOLOGY

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

them.

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

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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

time.

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

questions:

• 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?

(Intention);

• 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

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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

time.

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

Activity

Task

Device Physical object the visitor

carries with him

Network Infrastructure to connect

devices and convey

information

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

facts

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

services.

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.

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431

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.

5 CONCLUSIONS AND

IMPLICATIONS

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

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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.

ACKNOWLEDGEMENTS

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.

REFERENCES

Ay, F., 2007. Context Modelling and Reasoning using

ontologies.

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

European

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

Applications.

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.

HUMAN-CENTRIC ONTOLOGY-BASED CONTEXT MODELLING IN TOURISM

433

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,

428-432

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

Intelligence.

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.

http://www.wto.org

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. http://www.wttc.org

World Travel and Tourism Council, 2008. International

Recommendations for Tourism Statistics.

ST/ESA/STAT/SER.M/83/Rev.1

W3C, World Wide Web Consortium, 2004.

Composite/Capability/Preference Profiles (CC/PP):

Structures and Vocabularies 1.0. W3C

Recommendation 15 January 2004

http://www.w3.org/TR/CCPP-struct-vocab/

W3C, World Wide Web Consortium, 2008. Delievery

Context ontology. W3C Working Draft 15 April 2008.

http://www.w3.org/TR/dcontology/

KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development

434