TOURISM INFORMATION AGGREGATION USING AN

ONTOLOGY BASED APPROACH

Miguel Gouveia and Jorge Cardoso

Department of Mathematics and Engineering,Universaty of Madeira, Tecnopolo, Funchal, Madeira

Keywords: Information Aggregation, Data Integration, Tourism Information, Dynamic Package, Ontology and

Semantic Web.

Abstract: Aggregating related information, from different data sources, allows the creation of data repositories with

very useful information. In the tourism domain, aggregating tourism products with related tourism

attractions will add value to those products. The ability to create dynamic packages is another reason to

aggregate tourism information. Defining an ontology, composed by the concepts to aggregate, is the first

step to create tourism aggregation systems. In this paper we define the approach and the architecture that

guides to the creation of aggregated solutions that provide valued tourism information and that allow the

creation of dynamic packages.

1 INTRODUCTION

The Web became a large repository where one can

get information of all kinds. Some enterprises

embrace this opportunity and create large data

repositories. Enterprises like ChoicePoint, Experian,

LexisNexis or Acxiom are some examples. They sell

aggregated data that can help other enterprises to

manage their business. Information aggregation like

customer preferences, product prices and market

tendencies can help enterprises manage the risk and

reward of commercial and financial decisions.

ChoicePoint, one of the top companies selling

information, sells to more than half of America’s top

1000 companies.

The idea of data aggregation has being applied to

vast business areas, we believe that it will also have

a big success in the tourism industry. The tourism

domain is characterized by a significant

heterogeneity market and information sources and

by a high volume on online transactions (Werthner

and Klein, 2004). Nowadays, there is a lot of

information about tourism products throughout the

Internet and other systems. There are systems that

offer information about a set of tourism products

types like airlines, hotels and car rental. In this group

of systems we have the Computerized Reservation

Systems (CRS) that are associated to a specific

travel supplier and the Global Distribution System

(GDS) that is a super switch connecting several

CRSs (Cardoso, 2005). From the Hotel Distribution

Systems (HDS) we can get information about hotels.

There are also the Destination Management Systems

(DMS) that provide information about tourist

regions. Besides these sets of systems, there are

many web sites that offer tourism information that

aren’t assessable through any of the enumerated

systems. Web sites about hotels that belong to small

companies, car rental, golf or information about

tourist regions are just some examples.

Besides the tourism information aggregation, one

of the big challenges in the tourism business is

ability to create dynamic packages. Dynamic

package means putting together, in real time, a

package of several major travel components, e.g., air

flight legs, hotel nights, car rental days, etc (Kabbaj,

2003). It provides a single, fully priced package,

requiring only one payment from the consumer and

hiding the pricing of individual components within

5-15 seconds (Fitzgerald, 2005).

Current dynamic package applications are

developed using a hard-coded approach. However

such an approach for integration does not comply

with the highly dynamic and decentralized nature of

the tourism industry. Most of the players are small

or medium-sized enterprises with information

systems with different scopes, technologies,

architectures and information structures. This

diversity makes the interoperability of information

systems and technologies very complex and

constitutes a major barrier for emerging e-

569

Gouveia M. and Cardoso J. (2007).

TOURISM INFORMATION AGGREGATION USING AN ONTOLOGY BASED APPROACH.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - DISI, pages 569-572

DOI: 10.5220/0002371705690572

 SciTePress

marketplaces and dynamic applications that

particularly affects the smaller players (Fodor and

Werthner, 2004).

In this paper we will describe an architecture to

aggregate tourism information in order to provide

the creation of dynamic packages.

2 SEMANTIC TECHNOLOGY IN

INFORMATION

AGGREGATION

The process of information aggregation is not easy.

Currently Europe’s corporations spend over 10

billion Euros in dealing with data integration

problems (Alexiev, Breu, Bruijn, Fensel, Lara, and

Lausen, 2005). Companies are spending 10% to 30%

of their IT budgets on integrating applications and

systems internally and with their partners. The

problem with information aggregation is that the

information is not structured in the same manner.

Each data source, or application, has a different data

representation and provides different data formats

for integration. HTML, XML, flat files, relational

model are some of examples that we can find in an

aggregation problem. Another problem is the

semantic differences between data sources. We can

find the same word with different meanings. For

example, in one data source, customer can refer to

the tourists in others it can refer to the travel

agencies.

To resolve the information aggregation

problem, many technologies were proposed.

Database and application server vendors offer

comprehensive data integration tools and platforms.

However, they do not provide any support for

assuring semantic coherence and consistency of the

results (Alexiev, Breu, Bruijn, Fensel, Lara, and

Lausen, 2005). Using ontologies and data mapping

technologies, is it possible to resolve the semantic

incoherence. Ontologies aim at capturing static

domain knowledge in a generic way and provide a

common agreement upon the understanding of that

domain (Chandrasekaran, Josephson, and

Benjamins, 1999).

3 ONTOLOGY BASED

APPROACH

Information aggregation can remit us for two

integration approaches. In the first approach we can

start by selecting the data sources to integrate and

then try to create an ontology, based on the metadata

from the data sources to integrate. In this approach

we can follow the Semantic Information

Management Methodology (SIM) (figure 1).

Figure 1: The SIM Methodology.

In SIM methodology first we collect the

metadata of the existing data sources. Then, using

this metadata, a central ontology is created capturing

the meaning of the data presented in these data

sources. Finally, the disparate data schemas are

mapped to the ontology.

The second approach resides in thinking first in

the information that we want to aggregate and create

the ontology in order to create a useful knowledge

base. We call this the Ontology Based Approach

(figure 2). In this approach, the ontology is defined

not based on existing data sources metadata but,

instead, based on the solution that we want to build.

In the Ontology Based Approach, we begin with

the ontology definition. Based on the defined

ontology, we create the data schema that will be

used to integrate all the data sources. Then, the data

schema for integration is mapped to the Ontology.

Finally, we must search data sources that provide the

instances to populate the ontology.

Define

Ontology

Create Data

Schema

Integration

Mapping Data

Schema with

Ontology

Search and

Add Data

Sources to the

System

Utilize

Figure 2: Ontology Based approach.

4 TOURISM INFORMATION

AGGREGATION

ARCHITECTURE

In this section we describe our architecture for the

aggregation of information from different data

sources in the tourism domain (figure 3). The aim of

the architecture is to provide a framework that

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allows the aggregation of tourism information

following the Ontology Based Approach. The

framework must access tourism data sources, extract

their information, combine the data from the

different sources and present it to the tourist in an

aggregated form. The architecture is composed of

four layers. Each one of these layers will be

described next.

4.1 Semantic Layer

One of the most important components of the

architecture is the ontology. It is in the ontology

where we define all the concepts to aggregate. The

ontology must be defined in OWL language (OWL,

2004). Using the ontology elements we can define

rules. The rules must be defined in the Semantic

Web Rule language (SWRL, 2004). Creating the

rules in SWRL and not including them in the

ontology adds flexibility to the rules definition. In

run time we can activate or deactivate a specific

rule. The capability of rule definition is an essential

issue to allow dynamic packaging. We can define

rules that restrict the tourism packages or that add

discounts to a specific package definition. For

example, we can define that a person who chooses to

book a room in a specific hotel has a discount in a

specific restaurant. The rules are managed by the

RACER engine and will affect the result of the

information queries. All the instances presented in

the ontology must respect the defined rules. If a

specific instance does not respect the rules, then it is

removed from it.

To query the architecture we use the nRQL

language. The nRQL language is the semantic query

language used in the RACER engine. This language

allows query information from the ontology defined

in the OWL language.

4.2 Mapping Layer

All the data provided by the data sources must be

added to the ontology defined for the architecture.

This layer is responsible to transform the syntactic

information, defined in XML, in semantic

information, defined in OWL. The transformation

process uses an XSLT document to transform XML

data in OWL data. The XSLT is created using the

JXML2OWL tool (Rodrigues, Rosa, and Cardoso,

2006). This tool provides an interface that allows the

visual mapping between XML elements and OWL

elements. As a result of the mapping we get the

correspondent XSLT document.

The tool also provides the mapping rules stored

in an XML file. The mapping rules define all the

relation between XML elements and OWL elements.

These rules are used in the query transformation

process. The query transformation process has to

transform nRQL queries in syntactic queries. In the

transformation process we have to guarantee that all

the syntactic data will be extracted in order to

execute the semantic queries with success.

Figure 3: Tourism Information Aggregation Architecture.

DataSouce 1 DataSouce 2 DataSouce n

Gatherer

Mapping Tool

(

XML 2 OWL )

Tourism

Ontology

(OWL)

Transformation

Process

(XSLT)

Define

Transformation

Query

Tranformation

Add Ontology Instances

Data Access

Control

Semantic Query

Sintatic Query

XML data

Semantic

Data

External

Data Sources

Syntactic

Layer

Mapping

Layer

Semantic

TOURISM INFORMATION AGGREGATION USING AN ONTOLOGY BASED APPROACH

571

4.3 Syntactic Layer

In this layer we integrate all the data sources. We

use the Gatherer application (Silva and Cardoso,

2006) to perform the integration. Each one of the

data sources is registered in the architecture and

mapped to a pre defined XML schema. The XML

schema is the one used in the mapping layer. It is

created based in the ontology and is used to facilitate

the data sources integration.

For each data source to integrate we have to

create an XML structure that will define the data that

will fulfil a specific item of the XML Schema. Thus,

the Gatherer application knows where to get the

information for a specific query.

4.4 External Data Sources

This layer is composed of all the data sources that

will provide information to the architecture. They

can be Data Bases, XML files, Web Services or

simple Web Pages.

5 RELATED WORK

Semantic technologies were already used to resolve

data aggregation problems. TDS Biological Modeler

(Teranode, 2006) is a collaborative biology analysis

application that integrates heterogeneous data

sources in order to provide aggregated information

for scientific analyses. In the healthcare domain the

CEN/ISSS eHealth Standardisation Focus Group

integrates a set of information systems to allow the

exchange of meaningful clinical information among

healthcare institutes (Bicer, Laleci, Dogal, and

Kabak, 2005). Another example of success is the

COG project (Alexiev, Breu, Bruijn, Fensel, Lara,

and Lausen, 2005). The aim of this project is the

integration of a set of applications existing in an

automobile industry.

6 CONCLUSION

The presented architecture can be very useful to

create solutions that integrate different data sources

to fulfil a specific ontology. In the tourism domain,

the information must be aggregated in order to allow

the creation of dynamic packages. By using our

architecture, we can think first in defining the

information concepts that we want to aggregate.

Then, search for data sources that can provide the

information to integrate with them.

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