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Semantic Representation of Tourism on the Internet

Zheng Xiang

Assistant Professor

School of Merchandising and Hospitality Management

University of North Texas

P.O. Box 311100

Denton, Texas 76203-1100

Phone: 940-369-7680

Fax: 940-565-4348

Email: philxz@unt.edu

Ulrike Gretzel

Director and Assistant Professor

Laboratory for Intelligent Systems in Tourism

Department of Recreation, Parks and Tourism Sciences

Texas A&M University

2261 TAMU

College Station, TX 77843-2261

Phone: 979-862-4043

Fax: 979-845-0446

Email: ugretzel@tamu.edu

Daniel R. Fesenmaier

Director and Professor

National Laboratory for Tourism & eCommerce

School of Tourism and Hospitality Management

Temple University

1700 N. Broad Street, Rm. 201

Philadelphia, PA 19122-0843

Phone: 215-204-5612

Fax: 215-204-8705

Email: drfez@temple.edu

Acknowledgement: We would like to thank Dr. Bertrand J. Jansen at Pennsylvania State

University for his generosity in sharing the search engine transaction log files with us.

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Semantic Representation of Tourism on the Internet

ABSTRACT

With the huge amount of information available on the Internet and the increasing importance of

online search, understanding the tourism domain is essential for effective online marketing. This

study focuses on the semantic representation of the tourism domain with respect to information

provided on tourism-related websites and traveler’s information needs as expressed through

search engine queries. The results show that huge discrepancies exist between a domain ontology

derived from tourism websites and the one emerging from user queries. This study offers useful

insights into the challenge of representing tourism products and services through websites and

provides directions for developing Internet-based systems that can better support travel planning.

Keywords: Search engines; Semantic Web; ontology; semantic representation; online marketing.

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Semantic Representation of Tourism on the Internet

INTRODUCTION

The Internet can be considered a virtual “galaxy” with entities representing various kinds

of tourism information, among countless other domains. As such, there are many questions one

may ask regarding the nature of online travel information. Arguably, one of the most important

questions is whether the information currently available online matches the information needs of

travelers. Identifying structures and gaps in online travel information is important as it can

provide input for the design of websites and other Internet-based technologies to better support

travel planning (Carroll and Thomas 1982; Crawford 2003; Hevner et al. 2004; Norman 1999;

Werthner 1996). In addition, such knowledge is essential for developing effective strategies for

online tourism marketing (Adomavicius and Tuzhilin 2005; Fesenmaier, Wöber, and Werthner

2006; Riedl and Konstan 2002).

This paper builds upon a series of studies that have focused on understanding the online

tourism domain and the information needs and search strategies of its users (Fesenmaier et al.

2006; Gretzel and Wöber 2004; Pan and Fesenmaier 2006; Wöber 2006; Xiang et al. 2007;

Xiang, Wöber, and Fesenmaier forthcoming). In particular, Pan and Fesenmaier (2006) and

Xiang et al. (2007) demonstrated that the tourism domain is represented differently on the

Internet for tourism providers and consumers. Their findings, however, were based on small

experimental studies and provide very limited descriptions of tourism on the Internet. Therefore,

the goal of this study is to obtain a more comprehensive understanding of the online tourism

domain using larger, more representative data sets derived from actual search engines queries. It

is posited that the results of this study provide useful implications for the design of tourism

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specific search engines as well as the development of potentially more effective strategies for

online destination marketing.

ONLINE TOURISM DOMAIN ONTOLOGIES

Tourism is a field that manifests unique characteristics in the provision (supply) and

consumption (demand) of products (Smith 1988; Woodside and Dubelaar 2002). Tourism

businesses and tourism-related organizations such as destination marketing organizations present

and promote their offerings online using a language which is largely industry-specific (Dann

1997; Pan and Fesenmaier 2006). The demand side, on the other hand, consists of travelers and

their respective information needs as expressed through searches conducted online (Wöber 2006).

Thus, the online tourism domain is extremely large and complex and includes objects related to

both the tourism industry and the traveler (Pan and Fesenmaier 2006; Xiang et al. forthcoming).

In computer and information science, an ontology is defined as comprised of a

representational vocabulary with precise definitions of the meanings of terms plus a set of formal

axioms that constrain interpretation and well-formed use of these terms (Campbell and Shapiro

1995). Recent studies of Internet-based technologies have shown that an important limitation to

the use of the Internet relate to the domain representations (i.e., ontology) used to support online

search (Berners-Lee 1999; Kim 2002; Lau 2003). These studies indicate that a common

vocabulary is essential because it is used to bridge the various “languages” of computer systems

and users so that meaning (i.e., semantics) can be established and shared between the system and

the user. The notion of the “Semantic Web” has been developed to describe the Internet-based

infrastructure that supports the machine-understandable representation of the world (Berners-Lee

1999). From an information search perspective, emerging technologies such as search engines

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that have developed the ability to “understand” the semantics of user queries are only now

beginning to be able to answer questions such as: “Can you identify all destinations that have

beautiful beaches in the US?” As such, understanding the semantic representation of tourism and

its use in information search appears to be an important key to the further development of search-

related technologies and online marketing strategies in tourism.

There has been limited research focusing on online representation of the tourism domain

(Pan and Fesenmaier 2006; Wöber 2006; Xiang et al. 2007; Xiang et al. forthcoming). Wöber

(2006), for example, examined the visibility of destination marketing organizations and

individual hotel operations in Europe among six popular search engines. The findings of this

study showed that many tourism websites enjoy very low rankings among the search results,

which makes it extremely difficult for online travelers to directly access individual tourism

websites through these search engines. Pan and Fesenmaier (2006) found that the “language of

tourism” (Dann 1997) is extremely rich; further, their study indicated that the vocabularies used

on destination marketing organization websites differed substantially from those of potential

users. As such, they concluded that the richness in language and the differences in perspectives

make it very difficult for Internet users to have a satisfying online search experience. Following

from Pan and Fesenmaier (2006), Xiang et al. (2007) examined the language use among

consumers and suppliers in describing a specific type of tourism product, i.e., dining experiences.

The results of this study are consistent with Pan and Fesenmaier (2006) in identifying the

substantial differences in how users and producers perceived and represented travel-related

products on the Internet. Unfortunately, both studies were conducted in an experimental setting

by using a very limited number of student subjects. Therefore, these studies are not able to

describe the nature of the online tourism domain to a meaningful extent.

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It is argued that Pan and Fesenmaier's (2006) conceptual framework, however, provides a

useful foundation for understanding the way travelers utilize the tourism domain in constructing

a trip plan in that it describes the tourism domain as exhibiting a hierarchical, interconnected

structure which can be represented as a semantic network with nodes and interrelationships.

Importantly, Pan and Fesenmaier (2006) showed that quantitative textual analysis, particularly

semantic network analysis, provides a strong theoretical and methodological foundation with

which to describe the semantic nature of the online tourism domain. Quantitative textual analysis,

according to Krippendoff (2004), is underpinned by theories in communication and linguistics

with a fundamental assumption that relative frequency signifies the importance of a specific

word, and the meaning of the word is a function of the proximity to other words. In semantic

network analysis, the semantic structure, i.e., “meanings”, of text can be constructed and

represented in a variety of ways by establishing the associations between different concepts

(Woelfel 1993). Different measures such as proximity and centrality and the strengths of

identical relationships can be derived to measure the structure of a semantic network and to

compare the differences between two semantic structures (Carley 1997; Popping 2000).

Proximity is one of the most important measures in that it can be used to measure the

interrelationships between words within a semantic network. Another important measure is

“centrality”, which describes the “prominence” of a node within the overall network (Wasserman

and Faust 1994) whereby words with higher centrality values represent dominant “themes” in the

text. The similarities (or distance) matrix generated in text analysis can be used as input into

multivariate analyses such as cluster analysis and multidimensional scaling in order to assess

both the content and structure of a semantic network. For example, icicle maps in clustering

analysis and coordination maps derived from multidimensional scaling can be used to assess the

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meanings of textual data and are, thus, deemed useful for describing the semantic representation

of online tourism (Gretzel et al. 2008). Last, QAP (Quadratic Assignment Procedure) is an

analytical procedure that can be used to compare the structural properties of two networks and

establish similarity or dissimilarity (Krackhardt 1987).

RESEARCH QUESTIONS

This study posits that travelers’ use of search engines establishes the basis with which to

understand the nature of tourism domain ontologies. Search engines such as Google, Yahoo!, and

MSN index a huge number of Web pages on the Internet and thus serve as the “Hubble”

telescope with which people learn about the entire virtual “galaxy” (Castells 2001; Spink et al.

2002). Typically, the interaction between a traveler and a search engine interface begins when a

traveler with an initial search task and a particular mental model enters a query into a search

engine textbox. At this stage, the traveler's mental model consists of a set of factors including the

traveler’s understanding of how the system (search engine) works and knowledge of the domain

as well as the search task itself (Gretzel, Hwang, and Fesenmaier 2006) and where query

formulation involves the mapping of one’s mental model with the system selected (Marchionini

1997). Thus, a query can be seen as the expression of the user’s information needs in the context

of a search task.

Based upon the query, the search engine retrieves and returns a number of search results

that “match” the keyword string and displays them in a pre-defined format (Yu and Meng 2003).

If the search results match the traveler’s expectations, he/she will most likely examine the links

displayed on that page; if not, he/she may enter new keyword(s) to start another round of the

query – website selection process. Thus, this sequence of interaction with a search engine

interface involves the traveler’s reading and understanding of the results of the search with

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respect to a mental model, and then navigating back and forth between the search engine

interface and the travel information space (Jansen, Brown, and Resnick 2007). Based upon this

understanding, it can be expected that a mapping process occurs between the query terms used

by a traveler and the semantics on webpages that are considered relevant. This implies, then, that

the traveler makes a series of decisions based on the data (i.e., the descriptions included in the

search results as well as the webpages chosen by the traveler) to which he/she has been exposed.

As such, the use of search engines for travel purposes can be seen as the interaction between the

demand and the supply of tourism through one of the most important channels on the Internet.

Of particular interest to this study is the representation of the tourism domain through

search engine results, i.e., the supply, and the queries people use to search for tourism related

information, i.e., the demand. The following research questions were formulated to guide this

study:

Q1. What constitutes the online tourism domain as represented through a search engine?

Q2. What are the commonalities and differences between the language used in search

queries and the language on tourism websites?

RESEARCH METHODS

This study was conducted in three phases in order to address each of the three research

questions. As shown in Figure 1, Phase I focused on understanding the online tourism domain

from the supply perspective based upon its representation through a general purpose search

engine; Phase II, on the other hand, focused on gaining domain knowledge from the demand

perspective through tourism-related user queries using similar search engines. Finally, Phase III

built upon the results of Phase I and Phase II and compared the derived tourism domain

ontologies from tourism websites and user queries in order to understand the commonalities and

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differences between the representation of tourism from the demand and supply perspectives. This

section details the overall research design as well as data collection and analysis corresponding

to each of these three phases.

Insert Figure 1 about here

Research Design

A travel planning scenario was used to mimic travelers’ use of keywords to query a

search engine to plan a trip to a specific destination. Specifically, the city of Chicago was chosen

as a convenient case due to its status as one of the largest urban tourist destinations in the United

States as well as its diversity in cultural and historical resources for tourists. Then, a set of nine

pre-defined keywords (i.e., “accommodation”, “activities”, “area”, “attractions”, “events”,

“information”, “places”, “restaurants”, and “shopping”), that are most likely to be used by

travelers, were identified. In Phase I, these keywords, in combination with the destination name

(i.e., “Chicago”), were used to query Google. Based upon the URLs provided by the search

results, the text-based contents from tourism websites were extracted to represent the tourism

domain from the supply perspective. In Phase II, the same keywords, together with the

destination name (i.e., Chicago), were used as “seeds” with which to extract user queries from a

number of search engine transaction logs.

Two critical considerations guided the development of this approach. First, the pre-

defined keywords must reflect the tourism domain in a comprehensive way. The selection of the

keywords was guided by both the classification schemes used by the tourism industry and actual

queries used by travelers. Specifically, websites of several destination marketing organizations

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located throughout the United States were used as sources to identify these keywords. That is,

textual labels for the navigational menus on these websites were extracted to obtain the

categories by which DMOs organize their information (e.g., “accommodation”, “attraction”,

“events”, etc). Also, an analysis of the publically available query logs of a European-based

search engine (visiteuropeancities.info) were used to provide a triangulation with the types of

keywords likely to be used by travelers (Wöber 2006). After discounting cultural and geographic

specifics (e.g., the DMO of Chicago had “theatre” as one of the top-level categories while others

did not; and, churches and historic architectures were among the most searched items among

European travelers), these nine keywords consistently occurred in both the supply and demand

sources and, thus, were considered “generic” categories that represent online tourism.

Second, a number of search engines were selected to obtain data describing travel-related

search queries from the demand perspective. Google was used as the sampling frame for the data

collection for Phase I as it represents the state-of-the-art search technology on the Internet. Also,

it is ranked as the most popular search engine on the Internet with reportedly an index of around

25 billion Web pages and 250 million queries a day; this represents approximately half of all the

queries occurring on the Internet (Bertolucci 2007; Brooks 2004; Burns 2007). Further, it is

considered to be one of the most comprehensive text-based search engines on the Internet

(Bertolucci 2007). Phase II utilized a convenience sample of transaction log files from three

search engines that are available publically, namely Excite, AltaVista, and AlltheWeb, to

understand the semantic nature of tourism related queries (Jansen and Spink 2005). While these

search engines might be different from Google in certain aspects (i.e., ranking algorithms, etc.),

all of them have an interface similar to Google. That is, the interaction between a user and the

system is supported by a typical textbox for users to type in queries and the search results are

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represented in a “list” format. As such, it is argued that these search engines can be considered

essentially equivalent to Google, at least from a user system interaction viewpoint.

Data Collection

Data collection in Phase I followed a two-step procedure. First, the nine keywords were

used to form queries with the destination name (i.e., “Chicago”) and then the search results were

extracted from Google. According to the information retrieval literature (e.g., Spink et al. 2002),

most search engine users (>85%) do not view search results beyond the first three pages

(assuming each page contains 10 search results as the standard practice of most text-based search

engines). As such, the first 30 URLs which comprise the first 3 pages of search results are most

likely to be viewed by travelers. In addition, another two search result pages (i.e., with 20 search

results) were extracted, with one page from the middle of the search results and another page at

the bottom of the search result set, in order to provide a sample with more “depth”. Then, a Web

crawler program written in the Perl programming language was used to retrieve Web page

contents by following these 450 (50 search results * 9 search terms) URLs. The textual content in

the body of the Web pages were then parsed and saved as the “corpus” to represent the supply-

side domain.

In Phase II, user queries from three major search engines were used for the analyses (i.e.,

three sets of log files from Excite, one from AltaVista, and two from AlltheWeb, dated from

September 1997 to May 2002). The detailed descriptions of these transaction logs are provided in

a number of publications by Spink and her colleagues (Jansen and Molina 2006; Jansen and

Spink 2005, 2005; Jansen, Spink, and Pedersen 2005; Spink et al. 2004). Altogether, these

transaction logs included about 11 million distinct queries. However, only the nine pre-defined

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keywords in combination with the destination name “Chicago” were extracted, resulting in a

total of 3,020 observations (i.e., search queries).

Data Analysis

Quantitative text analysis was employed as the primary analytic approach followed a

three step design. Phase I focused on identifying the semantics that represent the tourism domain

based on the textual data extracted from tourism Web pages. Phase II focused on evaluating the

semantics that were used by search engine users looking for travel information for Chicago. Last,

Phase III compared the results from the previous two phases with a focus on assessing the

commonalities and differences in the words contained in the two data files.

Phase I: Assessing the Online Tourism Domain from a Supply-Side Perspective

The goal of this analysis was to understand the semantic nature of the tourism domain

from the supply perspective. It included two steps: Step 1 aimed to identify the words that truly

represent the domain, i.e., the domain ontology, and Step 2 aimed to understand the semantic

structure of this ontology, focusing on the central words that link others words together in the

text. In Step 1, a pre-processing procedure was carried out with the goal to identify the “stop”

words in the data including articles, prepositions, conjunctions, and transitive verbs that do not

contribute to the meaning of the text (e.g., “a”, “an”, “the”, “and”, “but”, and “also”, etc). Then,

the aggregated text file was imported into the statistical software SPSS to calculate the

frequencies of each unique word. An examination of the distribution of the frequencies of all the

unique words indicated that there are a huge number of unique words in the data and there are a

relatively small number of words that are used often. A cut-off value of the first 787 words with

the highest frequencies was used to include those that “best” represent the tourism domain for

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two reasons: 1) the cumulative frequencies of these words represent approximately 60% of the

total frequencies of all unique words; and, 2) the lowest frequency among this word is 45.

Considering this text represents 450 Web pages (3 X 50), words that have frequencies of less

than 45 only occur, on average, less than once in 10 Web pages. As such, it was assumed that

they were rarely used on tourism websites. These 787 words were then manually examined with

the goal to identify words that are “representative” of the tourism domain. Words that were

informative about the document itself such as “file”, “total”, and “copyright” were identified and

dropped from the pool. This resulted in a final pool of 364 words which were then used to

represent the online tourism domain for Chicago.

The next step of the analysis identified the semantic structure of the aggregated text using

the identified 364 words. Neural network software CATPAC (Woelfel 1993) was used to assess

the semantic associations between the respective words. In CATPAC the “neuron”, which

represents a pre-specified word, is initiated by passing a "scanning window" of n consecutive

words through the text while ignoring the pre-identified “stop” words (Woelfel 1993). The

proximity between the “neuron” and other words is measured and recorded. The program then

reads the next group of k words, depending on the slide size (i.e., the number of words by which

the scanning window moves). If the slide size is 1, for example, CATPAC moves one word

further in the text and then reads the next word, and so on, until the full text has been examined.

The software will continuously iterate through the full text with a new “neuron” until all words

in the pre-specified word set have been exhausted. The structure of the semantic association can

be represented by a square matrix of numbers where each row and column represents a neuron

(word), while the value of each cell (an updateable weight) represents the strength of connections

between the two neurons.

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The scanning window size is a critical facet of text analysis in that it has the potential to

impact the resulting semantic structure. Thus, an exploratory experiment was conducted to

determine the appropriate window size to use in the analysis. Specifically, proximity matrices

generated by CATPAC using various window sizes (from 1 – 7) were used as input into

hierarchical cluster analysis based on the criterion that the window size should not lead to too

many clusters (i.e., small clusters are disassociated with each other) or only one cluster (i.e., all

words are “lumped” together). A visual analysis of the results of the cluster analyses indicated

that a window size of 5 words was appropriate.

Since CATPAC does not output statistical measures for cluster adequacy and does not

provide the information about the clustering process (i.e., how and when words are linked

together), the proximity matrix was then imported into the social network analysis program

UCINET (Borgatti, Everett, and Freeman 1992) to further examine the clustering solutions. As

part of the analysis, centrality measures were calculated to identify words the most prominent

words as well as the semantic structure of the text.

Phase II: Assessing the Online Tourism Domain from the Demand Perspective

The primary goal of this analysis was to describe the semantic nature of user queries

related to Chicago tourism through search words extracted from 6 search engine transaction logs.

Analysis followed similar procedures used in Phase I, with the following distinctions after taking

into consideration the unique characteristics of search engine user queries (Spink 2002): 1) the

descriptive analysis not only identified the unique words in user queries but also examined the

length of queries; 2) the pre-processing of the data included identifying the stop words, deleting

sex-related queries and manually fixing user typos and misspellings; 3) the semantic association

was measured using CATPAC with a window size of 2 as the majority of queries were fairly

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short (1 – 4 words); 4) each query was treated as an individual case; and, 5) all unique words in

the dataset were included in the analysis.

Phase III: Assessing the Commonalities between the Online Tourism Domain and User Queries

The goal of Phase III analysis was to compare the semantic structures identified in the

first two phases of the study. Following Pan and Fesenmaier (2006), it is argued that if there is

high degree of commonality between these two structures, there is a “match” between the supply

and demand side, i.e., the information provided by the industry should be useful in helping users

find what they need. Comparing the words and the semantic relationships can, therefore, reveal

discrepancies between what is offered and promoted by the industry and what is searched for by

travelers. This analysis consisted of two steps; first, common words shared by the online tourism

domain and user queries were identified. A commonality ratio was then calculated based upon

the number of common words and the number of different words to show the degree to which the

online tourism domain and user queries have in common. Second, an N by N proximity matrix,

(where N stands for the number of common words) was constructed using CATPAC in the same

way as used in the first two phases for both the online tourism domain and user queries based

upon the common words. QAP was then used to assess the correlation between these two

proximity matrices (Borgatti et al. 1992).

RESULTS

The following sections present the results of the study corresponding to the three phases

of analysis: 1) the semantic nature of the supply-side tourism domain based upon Website texts

derived from search engine results (Phase I); 2) the semantic nature of the demand-side tourism

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domain based upon user queries in search engines (Phase II); and, 3) the comparison between the

domain ontologies identified based upon tourism related Web pages and user queries (Phase III).

The Semantic Nature of the Online Tourism Domain from a Supply Perspective

Figure 2 shows the distribution of all the unique words with their frequencies after a

natural logarithmic transformation. As one can see, the top 100 words represent approximately

30% of the total frequencies of all unique words; the top 500 words represent more than 50% of

the total frequencies of all unique words while the top 1,000 words represent more than 60% of

the total frequencies of all unique words. Also, it is important to note that there are a large

number of words that are singletons and represent more than 1/3 of the words used once and

about 2/3 of all unique words used 3 or fewer times. This indicates that the language describing

the tourism domain from the supply perspective is dominated by a small number of words but the

overall domain is extremely rich and largely idiosyncratic. A further analysis of the less

frequently used words showed that while there were a large number of words that did not

exclusively belong to the tourism domain (e.g., words that are part of the “natural language” such

as adjectives like “familiar” and “immediate”, adverbs like “specifically” and “commonly”, and

nouns like “transition” and “threat”), many of these words were proper nouns such as “cabrini”,

“blackwell”, “bloomingdale”, “zenith”, and “quiznos” and reflected place-specific concepts, thus

representing the place-based foundation of the tourism experience.

Insert Figure 2 about here

Based upon the distribution of frequencies, 364 unique words which represented approx.

45% of all unique words were used to represent the tourism domain ontology (not listed due to

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limitation of space).Their frequencies ranged from the highest (12,565) to the lowest frequency

(38) and represented a huge variety of businesses and services in the tourism industry. The word

“chicago” had the highest frequency among all words as it was used to define the geographic

boundary of the domain. Some of the other popular words were generic words as they related to

tourism; these included “place”, “area”, “information”, “map”, “travel”, and “tour.” Many other

words were specifically related to attractions and activities including “attraction”, “events”,

“center”, and “world” while others related to location such as “west” and “downtown.” Last,

some words suggested an interest in promotions such as “best”, “deal”, and “service”.

The Freeman Betweenness Centrality measure (Borgatti et al. 1992) was calculated to

identify those words that were prominent in the semantic network and played a central role by

linking different groups of words. Intuitively, this approach can be understood as finding those

words that were commonly shared among different Web pages. Table 1 lists the top 25 words in

the semantic network that have the highest betweenness centrality values. As can be seen, a

number of the words exhibited a relatively high centrality value (mean=164.3 with a standard

deviation of 1,238) and the overall network centralization was high (29.2%). This indicates that

certain concepts were highlighted in the representation of tourism services and products by the

industry and they were commonly “shared” across different websites and sections of websites.

Interestingly, it seems that these words can be placed into “layers” based on their centrality

values. For example, while the word “shop” has the highest betweenness centrality, words such

as “music”, “experience”, “famous”, “blues”, “theater”, “european”, “distance”, and “boutique”

have centrality values much lower than “shop” but much higher than the rest of the words.

Insert Table 1 about here

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The Semantic Nature of the Online Tourism Domain from a Demand Perspective

Most user queries were short, ranging from 1 to 4 words, and were either very general

(e.g., “Chicago hotel”) or very specific (e.g., “Chicago Wyndham hotel”). Figure 3 shows the

distribution of frequencies of unique words in user queries, indicating that the top 20 words with

the highest frequencies represented more than half (52%) of the total frequencies of all words

and the top 100 words represented 70% of the total frequency. As can be seen, approximately

two thirds of all the unique words had a frequency lower than or equal to 2 and 45% of all unique

words were used once by search engine users. Overall, the distribution was highly skewed

toward the small set of high frequency words, reflecting travelers’ general interests in tourism

related information (e.g., “hotel”, “direction”, “map”, “downtown”, “reservation”, “spa”,

“discount”, “accommodation”, “lakeview”, etc).

Insert Figure 3 about here

The Freeman Betweenness Centrality measure (Borgatti et al. 1992) was calculated to

identify the words that are prominent in the semantic network. Table 2 lists the top 25 words with

the highest betweenness centrality values and provides the descriptive summary of the

betweenness centrality measure among the semantic network of the 178 unique words. It is not

surprising to see that words such as “chicago”, “area”, “chicagoland”, “suburb”, “art”, and

“downtown” have the highest betweenness centrality values because they were used most often

in combination with other words. For example, the word “downtown” was often used together

with words such as “chicago”, “hotel” and “shop” to form more specific queries. The overall

network centrality was 2.2%, which indicates that the network has a limited degree of centrality,

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indicating that the network was somewhat connected and dominated by the words with high

centrality values.

Insert Table 2 about here

Comparing the Supply and Demand Ontologies

Table 3 lists the common words shared between the domain ontology and user queries.

As can be seen, there were 208 words in user queries that were also found in the domain

ontology derived from the supply side. While most of the common words apparently represented

the business facets in the industry, a number of adjective words (highlighted in bold) including

“fine”, “fun”, “unique”, “free”, “official”, “friendly”, “perfect”, “romantic”, “old”, “cheap”, and

“special” were also commonly used by both search engine users and the industry. It seems that

while these words were used by the industry with the intention to promote their products and

persuade potential visitors, search engine users had also used these words in order to locate

specific information about the products and services they wanted.

Insert Table 3 about here

Table 4 provides the ratios of the frequencies of these common words to the total

frequency of all words in both user queries and website results. As can be seen, there was a

relatively small proportion (17.3%) of query words that were actually represented in the website

results, while there was a relatively higher proportion (57.1%) of the words reflected in the

queries people used to search. Taking into account that many of these overlapping words had

high frequencies in both ontologies, the actual ratios that more accurately reflected their

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prominence were higher; that is, 33.1% for words in user queries and 79.2% in the website text,

respectively. However, there were still a large proportion of words in user queries (approximately

two-thirds) were not reflected in the supply ontology. An examination of these words revealed

that: 1) the words usually had low frequencies, suggesting that they were not popular “items”

people searched for in Chicago; and, 2) many of the words were proper names of an industry

entity such as the name of a restaurant, bar, or store. Last, QAP analysis (Krackhardt 1987) was

conducted using the proximity matrices constructed for the top 50 common words to assess the

correlation between the two ontologies (see Table 5). These words with relatively high

frequencies in both datasets were chosen to represent the core of the shared semantic space

between these two ontologies. The results indicate that no significant relationship existed

between the two semantic structures based on the 50 common words in the supply-side ontology

and the demand-side ontology based on user queries. Thus, this finding indicates that these two

semantic ontologies were structurally different, especially given that one would expect some

degree of communality among the most frequently used words.

Insert Tables 4 and 5 about here

CONCLUSIONS AND IMPLICATIONS

The results of the study indicate that the online tourism domain represented through

websites and travelers' search engine queries includes an incredibly rich amount of information

about the tourism industry within a destination. While the identified domain ontology is

relatively small because it focused on one destination, it is clear that the entire tourism domain is

rich and idiosyncratic with numerous destination specifics. That is, while there are a relatively

small number of words that dominate the tourism domain (e.g., travel, information, hotel, and

21

attractions), there is also a “long tail” with a huge number of words that reflects a wide range of

unique experiences that are offered at the destination. The findings also indicate that online

tourism information exhibits certain structural properties in that words representing the domain

are semantically associated. This semantic space not only contains the various dominant facets of

the tourism industry, but also connotes the meanings embedded in the semantic relationships.

Also, it seems that the tourism ontology contains core and peripheral spaces with certain words

being semantically closer to each other than others. For example, a number of words (e.g.,

“shop”, “music”, “experience”, “famous”, “blues”, “theater”, “european”, “distance”, “boutique”,

“premium”, and “contemporary”) were found to be highly central to the semantic structure of the

Chicago tourism ontology, suggesting their prominence in the semantic space of the domain and

their roles of bridging between clusters of words.

Analyses of tourism-related user queries from search engine transaction files showed that

the majority of queries are short and expressions of travelers’ information needs with the

intention to effectively and efficiently retrieve relevant information from search engines. Overall,

there are relatively few words in user queries that represent the majority of tourism related

“things” (e.g., “chicago hotel”). However, there is also a “long tail” of words that represent users’

heterogeneous information needs and their own mental maps of the tourism experience.

Importantly, these results appear to be consistent with previous studies of tourism information

search (Vogt and Fesenmaier 1998) whereby most information sought when planning travel is

functional rather than hedonic. That is, travelers are much more likely to focus on product

attributes such as location, price, and availability, instead of more experiential ones that are

based upon sensory and emotional aspects of the product (e.g., smell, atmosphere, and sensation,

etc). Also, user queries exhibit a strong semantic structure. That is, the types of information

22

people search for appear to reflect a spectrum of information needs ranging from very general to

highly specific. This finding is consistent with Pan and Fesenmaier (2006) who indicated that the

majority of users search for information that is very general (e.g., “Chicago hotel”), while a

relatively small number of them directly search for specific information by including the name of

the business (e.g., “Chicago Wyndham Hotel”).

The comparison between the ontologies of user queries and the information derived from

tourism websites indicate that there are a small number of words (N=208) common to the two

ontologies. While the majority of the words in the supply ontology were represented in user

queries, these common words represent only a small portion of all the words in user queries. The

differences in the words with high centrality values in the two semantic structures identified the

different orientations in the representation of tourism and users’ information needs. That is, the

results suggest that while the industry aims to promote businesses (such as “shop”, “boutique”)

by using persuasive words (“famous”, “premium”), travelers are more focused on information

about specific businesses or facts. In general, comparison between the ontologies indicate that

although the supply-side does reflect certain aspects of users’ information needs, there is a

substantial number of query terms that are not captured by this ontology.

The results of this study offer important implications for developing search technologies

and strategies for online marketing in travel and tourism. These technologies can be seen as

decision aides in travel planning, providing the means by which online travelers can simplify the

decision-making process by identifying the destinations and tourism businesses that meet the

traveler’s specific needs or desires (Fesenmaier et al. 2006). For a culturally rich domain like

tourism, the key in developing such technologies lies in a better understanding of the nature of

the domain and, consequently, meaningful ways to organize and represent the domain.

23

Specifically, the knowledge gained through this study suggests that new design approaches

should be identified with the aim to bridge the gap between travelers’ information needs and the

rich domain of online tourism. As shown in the empirical analysis in this study, the majority of

search engines users use very short and general queries in order to locate more specific and

relevant information. As such, a search system for tourism should establish a dynamic, more

flexible modality of interaction to allow the online traveler to articulate his/her needs.

Particularly, system feedback not only needs to include search results that are highly relevant to a

specific query but also should provide suggestions to “inspire” the online traveler by expanding

his/her consideration set. Typical examples for such techniques include the recommendation

mechanisms in Amazon’s search functions based on collaborative filtering and Google’s

contextual advertising based on mining user queries and search history (Gretzel and Wöber

2004). Because of the hierarchical structure within the supply-side domain ontology,

vocabularies in the domain can thus be used in the form of keyword association, for example, to

elicit travelers’ information needs or preferences by providing transitions from the general (e.g.,

“chicago hotel”) to the specific (e.g., “chicago downtown hotel with lakeview”). In addition,

consideration should be given to the interface that incorporates useful design factors such as the

visualization of the semantic structure of the domain, narrative logic and metaphors in order to

facilitate and enhance the user-system interaction to support travel search (Gretzel and

Fesenmaier 2002; Xiang and Fesenmaier 2006).

Also, the online tourism domain is understood as the symbolic transformation of tourism

products and experiences in the online environment. That is, the meanings of the domain

represent the purposeful communications between the industry and their prospective customers

in order to engender a positive image of the destination. In particular, the supply-side domain

24

ontology not only is comprised of vocabularies that represent various industry facets, but also

contains the words that the industry uses to describe their products and services. However, as

shown in this study this language is not necessarily used by travelers searching for trip related

information. As such, the development of innovative search technologies should focus on

establishing functions that “understand” the meanings connoted in this representation and

building mapping mechanisms between the supply- and demand-side perspectives. For example,

an intelligent system should be able to differentiate a user query which asks for a “spotless” hotel

room from those for a “reasonably clean” room (Markoff 2006). Thus, understanding the

language used by the travelers as well as the one used by the industry and building an

appropriate bridge between these domain ontologies is necessary for successfully mapping user

queries with industry website contents.

While search engines can play an important role in linking the supply and demand

ontologies of the tourism domain, tourism marketers can contribute to the success of travelers'

queries by better understanding the language used by travelers and adjusting their website

contents accordingly. That is, the best example of persuasive communication takes place on those

websites that address the specific information needs of the traveler using the “language” of the

traveler. It is argued that this language provides the foundation with which the prospective

traveler interprets the informational products offered by the industry. To achieve this goal, further

research and development is needed in a number of areas. Gretzel (2006), for example, suggests

that tourism marketers can turn to many of the readily available consumer generated contents,

e.g., blogs and reviews, to learn about the language travelers use to describe travel products and

their experiences. In addition, tourism websites can be designed to incorporate tools (e.g.,

reviews, tagging, and digging) that enable travelers to directly interact with them so that more

25

knowledge about the way travelers communicate their perceptions and experiences can be

collected and learned. It seems that these consumer-driven communication channels provide new

and promising avenues for tourism marketers to understand and therefore, better interact with

prospective visitors. Thus, it is argued that “the language of tourism” as reflected by both the

industry and the traveler provides an essential foundation necessary to guide the development of

technologies to needed to support travel planning on the Internet. However, much research is

needed to examine the potential impact of emerging technologies, changing demographics and

the roles of consumer knowledge and perception on this language.

26

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29

Goal Research Design Data Collection Data Analysis

Phase I

To understand language

representation of the

tourism domain in search

engines from the supply

perspective.

- Create a travel information search

scenario, i.e., by mimicking

travelers’ use of keywords to

query a search engine to plan a trip

to a destination, to collect textual

corpuses that represent the domain

from the supply and demand sides.

- Critical considerations included:

Selection of keywords that are

most likely to be used by

travelers

Selection of search engines for

data collection

- Use a set of keywords, which

are most likely used by travelers

for planning a trip to a specific

destination, to query Google.

- Extract a sample of 50 links

from search results for each

query and then textual content

from Web pages by following

these links to obtain the supply

side corpus.

- Identify words in Web page

content (i.e., the supply side

corpus) that represent the

domain ontology.

- Semantic network analysis

based on centrality measures

established through word

association.

Phase II

To understand language

representation of the

tourism domain in search

engines from the demand

perspective.

- Extract actual user queries from

a set of search engine log files

using the same destination

name and keywords set to

obtain the demand side corpus.

- Follow the same data analysis

procedure as Phase I to identify

the domain ontology in user

queries (i.e., the demand side

corpus).

Phase III

To understand the

similarities and differences

in the language

representation of the

tourism domain between

the demand and supply

perspectives.

- Compare the demand side and

supply side ontologies based on

the previous two phases.

- Ratios of common and different

words between the two

ontologies.

- Quadratic Analytical Procedure

to measure semantic similarity

between the two ontologies

based on a set of common

words.

Figure 1 An Illustrative View of the Research Methods

30

0

1

2

3

4

5

6

7

8

9

10

Chicago: 12,565

City: 2,148

Feature: 388

Luxury: 138

BurgerKing: 54

WGN: 21

Yorkville: 8

Zenith: 3

Quiznos: 2

Tourism: 297

Figure 2 Distribution of Unique Words on Tourism Websites

X-Axis: Number of Unique Words;

Y-Axis: Frequency of Words after Logarithmic Transformation

31

0

1

2

3

4

5

6

7

8

9

Chicago: 2,609

Airport: 61

Hotel: 277

HardRock: 21

Show: 8

Wine: 3Dinner: 2

Imax: 1

Bulls: 7McCormick: 6

Kimpton: 1

Figure 3 Distribution of Unique Words in User Queries (Logarithmic Transformation)

X-Axis: Number of Unique Words;

Y-Axis: Frequency of Words after Logarithmic Transformation

32

Table 1 Top 25 Words with the Highest Betweenness Centrality in the Supply-Side

Ontology

Word Centrality Word Centrality

shop 19316.9 museum 57.7

music 5401.4 cost 1.6

experience 5298.4 activity 1.6

famous 5135.0 jazz 1.6

blues 5033.9 craft 1.6

theater 4745.3 antique 1.6

european 4745.3 animal 1.6

distance 4518.0 kids 1.6

boutique 4518.0 amphitheater 1.6

premium 340.4 airlines 1.6

contemporary 152.0 diner 1.6

building 57.7 apparel 1.6

art 57.7

Total N: 364

Mean: 164.3

Std Dev.: 1,238.4

Min: 0

Max: 19,316.0

Network Centrality Index: 29.2%

33

Table 2 Top 25 Words with the Highest Betweenness Centrality in the Demand-Side

Ontology

Word Centrality Word Centrality

chicago 351.2 il 38.4

area 351.2 search 32.0

chicagoland 177.7 school 29.4

suburb 173.3 metro 24.4

band 143.9 fabulous 18.9

art 140.2 list 9.7

downtown 116.7 program 8.6

store 107.8 county 6.3

grade 98.2 service 4.7

janes 98.0 tribune 4.5

mirage 91.2 hotel 3.9

fine 41.8

land 55.4

Mean: 12.5

Std Dev.: 46.9

Min: 0

Max: 351.2

Network Centrality Index: 2.2%

34

Table 3 Common Words Shared between the Domain Ontologies

chicago travel phone expo card directions

university concert review facility catering feature

hotel inn show family contact fresh

area convention association food designer friendly

restaurant boat bike french dining garden

art reservation blues luxury dinner indian

mall clothing bus parking fashion japanese

park tour exhibit play flight jazz

map jewelry movie report free kitchen

theater public performance steak game landmark

store shopping activity tourism grill local

museum shore address zoo group mexican

info attraction basketball auditorium health musical

airport club buy business hours observatory

weather festivals company cheap kids official

events package gift college lodging perfect

downtown ticket golf cost medicine planetarium

train outlet indoor forecast nature plaza

guide schedule location fun pool population

school spa nightlife gallery resort quarter

shop book seafood loop sushi resource

church discount visit natural unique retail

football merchandise apparel network urban romantic

district water baseball party vegetarian room

pizza history bed rate weekend shoes

bar holiday breakfast style ads special

sports old children traffic amusement temperature

building rental chinese wine bakery thingstodo

library sites cook admission books trip

city aquarium live adventure brunch tv

field conference plan africa calendar vacation

cafe fine price antique casino video

place market sightseeing arena deal village

program motel tourist broadway deli

service music cultural bureau diner

The words are displayed in vertical order ranging from high to low frequency in

user queries; bold words are common adjectives uses by industry and users

35

Table 4 Ratios of Common Words Shared between the Domain Ontologies

Number of Words Total Frequencies

Common

Words

All

Words Ratio

Common

Words

All

Words Ratio

Supply

Ontology 208 364 57.1% 65,678 82,928 79.2%

Demand

Ontology 208 1,204 17.3% 3,242 9,788 33.1%

36

Table 5 QAP Analysis of the Semantic Structures of Top 50 Common Words

in the Supply and demand Domain Ontologies

Value Significance Average

Standard

Deviation P(Large) P(Small)

Pearson Correlation: 0.184 0.239 0.003 0.302 0.084 0.675

Simple Matching: 0.000 1.000 0.000 0.000 1.000 1.000

Jaccard Coefficient: 1.000 1.000 1.000 0.020 1.000 1.000

Goodman-Kruskal Gamma: 0.000 0.000 0.000 0.000 0.000