Semantic Web and E-Tourism

Semantic Web and E-Tourism

Danica Damljanovic (University of Sheffield, UK) and Vladan Devedžic (University of Belgrade, Serbia)
DOI: 10.4018/978-1-60566-026-4.ch544
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Abstract

Offering tourist services on the Internet has become a great business over the past few years. Heung (2003) revealed that approximately 30% of travelers use the Internet for reservation or purchase of travel products or services. Classic sites of tourist agencies enable users to view and search for certain destinations and book and pay for vacation packages. At a higher level of sophistication are tourism Web portals, which integrate the offers of many tourist agencies and enable searching from one point on the Web. Still, when using this kind of systems one is forced to spend a lot of time analyzing Web content with destinations that match his/her wishes. This problem is identified by Hepp, Siorpaes and Bachlechner (2006) as the “needle in the haystack” problem. Applying artificial intelligence (AI) techniques in E-tourism could help resolve this problem by providing: 1. Data that are semantically enriched, structured, and thus represented in a machine readable form; 2. Easy integration of tourist sources from different applications; 3. Personalization of sites: the content can be created according to the user profile; 4. Improved system interactivity. As an example of using AI in e-tourism, we present Travel Guides—a prototype system that offers tourists complete information about numerous destinations. They can search destinations by using several criteria (e.g., accommodation type, food service, budget, activities during vacation, and user interests: sports, shopping, clubbing, art, museum, monuments, etc.). He/She can also read about the weather forecast and events in the destination. In a way, Travel Guides complements traditional information systems of tourist agencies. These systems require a lot of maintenance effort in order to keep the huge amount of data about tourist destinations up-to-date. Travel Guides is created to minimize the user’s input and his/her need to filter information. It shows how usage of semantically enriched data in a machine readable form can Increase interoperability in the area of tourism, Decrease maintenance efforts of tourist agents, and Offer tourists a better service. Nowadays, there are just a few e-tourism systems that use AI techniques. We briefly discuss them in the next section. In this article, we explain why it would be good to use such techniques and how Travel Guides does it. Specifically, using Semantic Web technologies in the area of tourism can improve already existing systems (which are mostly available online) that do not use Semantic Web techniques yet. Likewise, the Semantic Web approach can help decrease the maintenance efforts required for existing e-tourism systems and ease the process of searching for vacation packages. Travel Guides was initially developed as a large-scale expert system. Over time, it has evolved into a modern Semantic Web application.
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Introduction

Offering tourist services on the Internet has become a great business over the past few years. Heung (2003) revealed that approximately 30% of travelers use the Internet for reservation or purchase of travel products or services.

Classic sites of tourist agencies enable users to view and search for certain destinations and book and pay for vacation packages. At a higher level of sophistication are tourism Web portals, which integrate the offers of many tourist agencies and enable searching from one point on the Web. Still, when using this kind of systems one is forced to spend a lot of time analyzing Web content with destinations that match his/her wishes. This problem is identified by Hepp, Siorpaes and Bachlechner (2006) as the “needle in the haystack” problem.

Applying artificial intelligence (AI) techniques in E-tourism could help resolve this problem by providing:

  • Data that are semantically enriched, structured, and thus represented in a machine readable form;

  • Easy integration of tourist sources from different applications;

  • Personalization of sites: the content can be created according to the user profile;

  • Improved system interactivity.

As an example of using AI in e-tourism, we present Travel Guides—a prototype system that offers tourists complete information about numerous destinations. They can search destinations by using several criteria (e.g., accommodation type, food service, budget, activities during vacation, and user interests: sports, shopping, clubbing, art, museum, monuments, etc.). He/She can also read about the weather forecast and events in the destination.

In a way, Travel Guides complements traditional information systems of tourist agencies. These systems require a lot of maintenance effort in order to keep the huge amount of data about tourist destinations up-to-date.

Travel Guides is created to minimize the user’s input and his/her need to filter information. It shows how usage of semantically enriched data in a machine readable form can

  • Increase interoperability in the area of tourism,

  • Decrease maintenance efforts of tourist agents, and

  • Offer tourists a better service.

Nowadays, there are just a few e-tourism systems that use AI techniques. We briefly discuss them in the next section. In this article, we explain why it would be good to use such techniques and how Travel Guides does it. Specifically, using Semantic Web technologies in the area of tourism can improve already existing systems (which are mostly available online) that do not use Semantic Web techniques yet. Likewise, the Semantic Web approach can help decrease the maintenance efforts required for existing e-tourism systems and ease the process of searching for vacation packages.

Travel Guides was initially developed as a large-scale expert system. Over time, it has evolved into a modern Semantic Web application.

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Background

According to Aichholzer, Spitzenberger and Winkler (2003), e-tourism comprises electronic services which include:

  • Information services (e.g., destination, hotel information);

  • Communication services (e.g., discussion forum);

  • Transaction services (e.g., booking).

Transaction services are offered at many places on the Web, such as Expedia, Travelocity, and so forth. These Websites include some of the information services, but for complete details about certain destination (e.g., activities, climate, monuments, and events) one must search for other sources. Some Websites even help in planning the whole itinerary (e.g., HomeAndAbroad). Apparently, there is an “information gap” between these online services, and no interoperability. Semantic Web technologies can be used to overcome this problem and thus increase the quality of e-tourism.

Key Terms in this Chapter

OWL-Based Web Service Ontology (OWL-S): An ontology which supplies Web service providers with a core set of constructs for describing the properties and capabilities of their Web services in unambiguous, computer-interpretable form.

Intelligent Reasoning: The act of using reason to derive a conclusion from certain premises using a given methodology. Location Based Services: Services that provide context-sensitive information based on the mobile user’s location.

Web Service Modeling Ontology (WSMO): A data model that provides the conceptual underpinning and a formal language for semantically describing all relevant aspects of Web services in order to facilitate the automation of discovering, combining and invoking electronic services over the Web.

Semantic Web Services: Self-contained, self-describing, semantically marked-up software resources that can be published, discovered, composed and executed across the Web in a task-driven semiautomatic way.

Intelligent Agents: Software elements which help the user find information of specific interest to him/her without their explicit assistance.

Ontology: A controlled vocabulary that describes objects and the relations between them in a formal way, and has a grammar for using the vocabulary terms to express something meaningful within a specified domain of interest.

Web Portal: A Web site or service that offers a broad array of resources and services, such as e-mail, forums, search engines, and online shopping malls.

Dynamic Packaging: The combination of different travel components, bundled and priced in real time, in response to the requests of the consumer or booking agent.

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