Supporting non-English Web searching: An experiment on the

Decision Support Systems 42 (2006) 1697 – 1714
www.elsevier.com/locate/dss
Supporting non-English Web searching: An experiment on the
Spanish business and the Arabic medical intelligence portals
Wingyan Chung a,⁎, Alfonso Bonillas b,1 , Guanpi Lai b,1 , Wei Xi b,1 , Hsinchun Chen b,1
a
Department of Information and Decision Sciences, College of Business Administration, The University of Texas at El Paso,
500 W. University Avenue, El Paso, TX 79968, USA
b
Artificial Intelligence Lab, Department of Management Information Systems, The University of Arizona, 1130 East Helen Street,
McClelland Hall 430, Tucson, AZ 85721, USA
Received 3 March 2005; received in revised form 19 February 2006; accepted 22 February 2006
Available online 27 June 2006
Abstract
Although non-English-speaking online populations are growing rapidly, support for searching non-English Web content is
much weaker than for English content. Prior research has implicitly assumed English to be the primary language used on the Web,
but this is not the case for many non-English-speaking regions. This research proposes a language-independent approach that uses
meta-searching, statistical language processing, summarization, categorization, and visualization techniques to build high-quality
domain-specific collections and to support searching and browsing of non-English information. Based on this approach, we
developed SBizPort and AMedPort for the Spanish business and Arabic medical domains respectively. Experimental results
showed that the portals achieved significantly better search accuracy, information quality, and overall satisfaction than benchmark
search engines. Subjects strongly favored the portals' search and browse functionality and user interface. This research thus
contributes to developing and validating a useful approach to non-English Web searching and providing an example of supporting
decision-making in non-English Web domains.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Internet; Web; Searching; Browsing; Business intelligence; Medical intelligence; Spanish; Arabic; Non-English Web searching; Web
portal; Mutual information; Summarization; Categorization; Visualization; Kohonen self-organizing map
1. Introduction
The Internet has gained popularity worldwide and is
estimated to continue to grow as access to Web content
in different languages increases. A report published in
⁎ Corresponding author. Tel.: +1 915 747 5496; fax: +1 915 747
5126.
E-mail address: [email protected] (W. Chung).
1
Tel.: +1 520 621 2748; fax: +1 520 621 2433.
0167-9236/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.dss.2006.02.015
September 2004 shows that the majority (64.8%) of the
world's online population consists of non-English
speakers [13]. Moreover, that population was estimated
to grow significantly in the near future to 820 million
while the size of English-speaking online population
was predicted to remain at 300 million [12]. For
instance, there are more than 3.5 million Internet users
in the Arab world [1] where the growth of Arabic Web
content is estimated to double every year [28]. The
Spanish-speaking online population has exceeded
9 millions and Latin America is estimated to have the
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
fastest growing population in the world in the coming
decades [2].
These statistics suggest a growing need for better
support for Web searching in some non-English languages that individuals and organizations use on a daily
basis. Non-English-speaking Internet users use their
native language to search for useful information, and
such searching typically happens across different regions
where the language is used, as is the case for members of
multinational organizations (MNOs) that have operations
in multiple regions using the same language. These
MNOs increasingly rely on the Internet when seeking
information. An example might be searching for
opportunities to expand a business in Latin America. A
medical institution in an Arab region may need to discover efficient ways to collect, analyze, and disseminate
massive information about different regions in the Middle
East and North Africa.
Despite growing needs for non-English Web searching,
most existing technologies have been developed for
English-speaking users and fail to address the needs of
non-English Web searching. Current search engines in
Spanish and Arabic, for example, lack search and analysis
capabilities. In particular, these search engines lack highquality collections to support searching across different
regions. Better approaches to overcoming these problems
would provide system developers with insights to enhance
non-English Web searching. To address the needs, we
propose in this paper a language-independent approach to
building intelligent portals in non-English languages. Our
goal was to develop and validate the approach to nonEnglish Web searching. Based on the approach, we
developed two Web search portals for the Spanish
business and Arabic medical domains. We empirically
studied the way these portals support decision-making and
the related issues using native Spanish and Arab subjects.
The rest of the paper is structured as follows. Section 2
surveys previous research in non-English Web searching
and search support in different languages. Section 3
presents a language-independent approach to supporting
non-English Web searching and the two Web portals
developed using the approach. Section 4 describes the
methodology for evaluating the portals. Section 5 reports
and discusses the findings. Section 6 concludes the paper
and discusses future directions.
2. Literature review
Since the inception of the Internet, English has been
the dominant language for communication on the Web.
Prior research about Web searching has assumed
implicitly that technologies are developed for English-
speaking users. However, as more non-English-speaking
users have adopted Internet technologies, other languages
have gained popularity. It therefore is useful to review
previous research in information seeking on the Web in a
multilingual world. In particular, we also review developments of Web search technologies for the Spanishspeaking and Arabic-speaking regions.
2.1. Information seeking on the Web
Researchers who have studied information seeking on
the Web have described the process of information
seeking as consisting of various stages of problem identification, problem definition, problem resolution, and
solution presentation [39]. Variations of this process
model can be found in the literature [18,22,35].
Two major information-seeking activities are searching and browsing. Prior research has considered searching
to include behaviors ranging from goal directed information searching, where the user has a specific target in
mind, to more serendipitous or exploratory information
browsing when no specific goal is present besides the
intention to explore the information repository [35]. In
directed searching, the user first decomposes his goal into
smaller problems, then expresses his needs as concepts
and higher level semantics, formulates queries using such
supports as Boolean query languages and syntax directed
editors, and finally evaluates the results by serial search or
systematic sampling. In exploratory browsing, the user
first transforms his general information need into a problem. He then (1) articulates that need as search terms or
hyperlinks that appear on the system interface; (2) searches using the terms or explores the hyperlinks using such
browse supports as automatic summarization, clustering
and visualization tools, and Web directories; and (3)
finally evaluates the results by scanning through them.
2.1.1. Support for Web searching and browsing
To support Web searching and browsing, various types
of information technologies have been proposed. Metasearching has been found to be a promising method [4] to
alleviate biases of search results from different search
engines [26] by sending queries to multiple search engines
and collating the set of top-ranked results from each
engine. In addition, post-retrieval analysis provides added
value to results returned by search engines. Previews and
overviews of retrieved Web pages are important elements
in post-retrieval analysis. A preview is extracted from, and
acts as a surrogate for, a single object of interest [14].
Document summarization techniques provide previews of
individual Web pages in the form of indicative summaries
[10], query-biased summaries [36], or generic summaries
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
[25]. An overview is constructed from and represents a
collection of objects of interest [14]. Document categorization techniques such as the self-organizing map
algorithm [17] have been used to categorize and search
Web pages [5]. Document visualization techniques also
have been used to amplify human cognition in browsing
Internet search results [20,23]. Despite the potential
advantages of meta-searching and information previews
and overviews, they rarely have been applied to nonEnglish search engines. One such application is the
CBizPort that helps users to search, browse, categorize,
and summarize Chinese business information [7] but does
not contain a domain-specific collection and lacks useful
functionality such as visualization.
2.1.2. Information quality
Information quality, a multifaceted concept, is considered to be an important aspect of evaluating the quality of
a Web site [21] and is one that has been explored by Wang
and Strong [38], who evaluated information quality using
a set of 16 dimensions that were tested in [33]. These
dimensions were for the most part used in evaluating the
quality of information of organizations or companies, not
the quality of information obtained from search engines.
Although Marsico and Levialdi [24] have developed a
Web site evaluation methodology that considers a site's
information quality, their methodology was designed for
evaluating general Web sites (e.g., travel information Web
sites) and does not consider the special requirements of
non-English Web searching.
There have been studies on cross-regional use of
Chinese search engines (e.g., [7]), but because Chinese is
mainly used in three geographically close regions
(mainland China, Hong Kong, and Taiwan), its regional
characteristics are less apparent than those of Spanish and
Arabic, which are used across continents and widelyseparated regions. Unfortunately, no attempts have been
made to study the cross-regional impacts of Spanish and
Arabic search engines, evaluation of which could improve
understanding of optimal design of search engines and
portals.
2.2. Search engines for Spanish-speaking and Arabicspeaking regions
As more non-English-speaking people use the
Internet to search and browse information, major
search engines have attempted to expand their services
for non-English speakers. Regional search engines that
provide more localized searching have begun to
emerge. In addition to English, these search engines
typically accept queries in a user's native language and
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return pages from the regions being served. A survey of
major search engines in Spanish and Arabic, two
widely-used languages that are gaining popularity on
the Web, follows.
2.2.1. Spanish search engines
Major search engines have been developed for
Spanish, the second most popular language in the United
States and the primary language for Spain and some 22
Latin American countries. Terra (http://www.terra.com/)
offers its services to more than 3.1 million Internet users in
Europe and the Americas. A Gallup poll in 2002 reported
Terra to be the most popular search engine in Spain;
Wanadoo (http://www.wanadoo.com/), a subsidiary of
France Telecom, was rated second [11]. Currently, Terra
serves more than 3 million Internet users in Spain, Latin
America, the United States, and many European
countries. Supporting Web searching in English and
French as well as Spanish, Wanadoo is currently the
leading Internet service provider in France and the United
Kingdom with 9.3 million customers in June 2004.
Spanish search engines serving Latin America include
Yahoo Español, Ahijuna, Auyantepui, Quepasa, Bacan,
and Conexcol. Yahoo Español (Spain, http://espanol.
yahoo.com/), the Spanish version of Yahoo, provides a
human-compiled Web directory developed by about 150
editors who categorized over one million listed sites.
YahooES also supplements its results with those from
Inktomi and Google. Inktomi matches also appear to users
after all YahooES matches have first been shown. Established in 1995, BIWE (Buscador en Internet para la web
en Español, http://www.biwe.com/) is one of the earliest
search engines for searching Spanish information on the
Web. BIWE supports searching of news, products,
images, and other information and provides a variety of
services including a Web directory, email, entertainment,
and market information for Hispanics. Headquartered in
the United States, Quepasa (http://www.quepasa.com/)
was launched in 1997 and is a bilingual Web portal
(Spanish and English) serving Hispanic populations in the
United States and Latin America. It uses proprietary Web
search technologies to reduce the number of irrelevant
results by utilizing terms most frequently used and
documents most frequently viewed [32]. Quepasa also
offers other services such as news, email, online radio,
chat, online translation, forums, and Web hosting.
The following Spanish search engines primarily serve
their own or adjacent regions. Launched in 1998,
Ahijuna (Argentina, http://www.ahijuna.com.ar/) provides searching services of Argentina Web sites and other
Spanish Web sites. It contains a Web directory with 14
categories having a total of 7578 hyperlinks. Based in
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Venezuela, Auyantepui (http://www.auyantepui.com/)
provides a searchable Web directory of Spanish sites. It
grew from 14 categories listing 117 Web sites in 1996 to
550 categories with over 18,000 Web sites in 2002.
Launched in 1998, Conexcol (Colombia, http://www.
conexcol.com/) provides a searchable Web directory
containing 14 categories having 400 subcategories and
13,214 Web sites' URLs. With more than 150,000
unique visitors per month, it is one of the top four most
visited sites in Colombia. Bacan (Ecuador, http://www.
bacan.com/), a major search engine in Ecuador, began its
operations in 1996. It provides services such as news,
email, online chat, entertainment, and shopping guides.
Every month Bacan has 80,000 individual visitors and
generates over 2 millions hits. Ascinsa Internet (http://
www.ascinsa.com/) is widely-used in Peru and contains
Web sites from Latin American countries and the United
States. It provides services such as Internet access, email,
Web page design, domain registration, Web hosting,
among others. It also contains a directory listed by
countries and then by domains.
Table 1 summarizes the content and functionality of
major Spanish search engines. Although different types of
information are provided, these search engines typically
present results as a long textual list and lack post-retrieval
analysis capabilities. Moreover, except for some large
Table 1
Comparing major Spanish search engines
Content
Spain
Web pages and
news on
IT
Business
Government
Financial
Medical
Other Latin
American
countries
General
Size of
collection
Terra
(Spain)
✓
✓
✓
✓
✓
Functionality
Latin America
Wanadoo
(France)
✓
✓
✓
✓
Auyantepui
(Venezuela)
✓
✓
✓
✓
✓
✓
✓
✓
Very good Very good Fair
Terra
(Spain)
Links to related ✓
resources
Membership
✓
services
Newsgroup
✓
search
Web directory
Search for Web ✓
sites
Search stock
✓
prices
Filtering for
adult content
Online
translation
tool
Search for
✓
news
✓
Multimedia
search
(image,
music,
software,
etc)
User interface Very good
Wanadoo
(France)
✓
Ascinsa
(Peru)
✓
✓
✓
✓
✓
✓
Conexcol
(Colombia)
✓
✓
✓
✓
✓
✓
Bacan
(Ecuador)
✓
✓
✓
✓
✓
✓
Good
✓
Good
✓
Fair
Quepasa
(Mexico and U.S.)
✓
✓
✓
✓
✓
✓
Very good
YahooES
(Spain)
✓
✓
✓
✓
✓
✓
✓
Very good Fair
Auyantepui Ascinsa Conexcol Bacan
Quepasa
YahooES
(Venezuela) (Peru) (Colombia) (Ecuador) (Mexico and U.S.) (Spain)
✓
✓
✓
✓
✓
✓
✓
✓
Ahijuna
(Argentina)
✓
✓
✓
✓
✓
BIWE
(Spain)
✓
✓
✓
✓
✓
✓
✓
Very
good
Ahijuna
BIWE
(Argentina) (Spain)
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Fair
Fair
Good
Very good
Very good Fair
Very good Fair
Very good
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
portals such as Yahoo Español, BIWE, and Terra, most
Spanish search engines serve a few regions rather than an
entire Spanish speaking community.
2.2.2. Arabic search engines
Arabic is spoken by more than 284 million people in
about 22 countries. Although Arabic is the fifth most
frequently spoken language in the world, the Arabic Web is
still in its infancy, constituting less than 1% of the total Web
content and having a low 2.2% penetration rate [1]. The
cross-regional use of Arabic and the exponential growth of
Arabic Web [28] nevertheless have highlighted the
necessity of providing better Web searching and browsing.
Four major search engines offer the Arab World
comprehensive services and extensive content coverage.
Ajeeb (http://www.ajeeb.com/) is a bilingual Web portal
(English/Arabic) launched in 2000 by Sakhr Software
Company. Its database contains over one million
searchable Arabic Web pages, which can be translated
to English using the online version of Sakhr's machinetranslation software. In addition, Ajeeb has a multilingual
dictionary and is known for its large Web directory, “Dalil
Ajeeb,” which the company claims is the world's largest
online Arabic directory. Ajeeb has launched Johaina, an
automatic tool that gathers news from many Middle
Eastern and worldwide news agencies. Using Sakhr's
“IDRISI” search engine Johaina gathers mainly Middle
East related news and categorizes them into primary and
secondary topic categories. Albawaba.com (http://www.
albawaba.com/) is a consumer portal offering comprehensive services including news, sports, entertainment,
e-mail, and online chatting. The portal supports searching
for both Arabic and English pages and the results are
classified according to language and relevancy. Albawaba
also provides meta-searching of other search engines
(Google, Yahoo, Excite, Alltheweb, Dogpile) and a
comprehensive directory of all Arab countries. Launched
in 2000, UAE-based Albahhar (http://www.albahhar.
com/) provides a wide range of online services such as
searching, news, online chatting, and entertainment. The
portal searches its 1.25 million Arabic Web pages and
provides Arabic speakers a wide range of other online
services like news, chat, and entertainment. Based in New
Hampshire, Ayna (http://www.ayna.com/) is a Web portal
providing an Arabic Web directory, an Arabic search
engine, and other services such as a bilingual (English/
Arabic) email system, chat, greeting cards, personal
homepage hosting, and personal commercial classifieds.
In July 2001, Ayna had over 700,000 registered users and
provided access to more than 25 million pages per month.
Due to Ayna's popularity, Alexa Research ranks it among
the top three leading Web sites in the Arab World.
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Table 2
Comparing major Arabic search engines
Content
Ajeeb
Albawaba
Albahhar
Ayna
Business
Government
Financial
Medical
General
Size of collection
✓
✓
✓
✓
✓
Very
good
✓
✓
✓
✓
✓
Good
✓
✓
✓
✓
✓
Very
good
✓
✓
✓
✓
✓
Very
good
Functionality
Ajeeb
Albawaba
Albahar
Ayna
Encoding conversion
(utf8-CP1256)
Links to related
resources
Membership services
Web directory
Search for Web sites
Search by time period
Search for news
Languages of the
search database
Cross-regional search
support
User interface
System reliability
✓
✓
✓
✓
Very
good
✓
✓
Very
good
✓
✓
English/
Arabic
✓
Very
good
Fair
✓
✓
Fair
✓
Good
✓
✓
English/
Arabic
✓
✓
✓
✓
English/
Arabic
✓
Very good
Good
Good
Fair
Poor
Very
good
✓
English/
Arabic
✓
Table 2 compares the content and functionality of the
Arabic search engines mentioned above. Despite their rich
content and comprehensive services, Arabic search
engines lack post-retrieval capability and their contents
tend to be general, offering limited resources to serve
domain-specific needs. They also fall short of supporting
advanced search and browse functions. For example,
none of them supports categorization or visualization of
search results.
2.3. Summary
Because existing search engines in Spanish and Arabic
typically lack analysis capabilities, they limit users'
ability to understand retrieved results. The collections
searched by these search engines are often regionspecific, so they do not provide a comprehensive
understanding of the environment where they are
operating. Major English search engines such as Google
provide searching of non-English resources but fall short
of covering domain- and region-specific information.
There is a need for better approaches to overcoming these
problems and to providing high-quality information to
multinational organization users. We therefore propose a
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
language-independent approach to address the following
questions:
1. How can a language-independent approach support Web searching in non-English languages that
are widely used across different regions?
2. How well do portals developed by the approach
perform in comparison with existing search
engines, in terms of accuracy, precision, recall,
and user satisfaction?
3. When comparing with existing search engines,
what is the information quality of the portals
developed by the approach?
in the forms of collated search results obtained from
various high-quality information sources, Web page
summaries, categorized search results, and visual maps
showing clusters of Web pages. Table 3 provides topical
coverage details of the two portals, screen shots of
which are shown in Figs. 1 and 2.
3.2. Steps in the approach
Our approach consists of five major steps, which are
described in the context of building SBizPort and
AMedPort.
3. A language-independent approach
Table 3
Topical coverage details of the two portals
In this section, we describe a language-independent
approach to supporting non-English Web searching. The
approach uses meta-searching, statistical language processing, summarization, categorization, and visualization
techniques to build high-quality domain-specific collections and to support searching and browsing of nonEnglish information on the Web. Specifically, we used the
approach to build two Web portals providing domainspecific collections for non-English Web searching and
post-retrieval analysis for the Spanish business and Arabic
medical domains. Because the implementation of the
approach requires no (or minimal) customization to the
portals' languages, it allows system developers to easily
adapt the development to new languages and domains.
Topics
3.1. The SBizPort and AMedPort
The chosen domains of the two portals, Spanish
Business Intelligence Portal (SBizPort) and Arabic
Medical Intelligence Portal (AMedPort), represent
important segments of the Web of interest to individual
users and multinational organizations. Given the
growing Spanish-speaking populations in the United
States, Spain, and Latin America, businesses actively
expand their opportunities by seeking information on
the Web. Meanwhile, the growing Arabic online
population and medical professionals seek a comprehensive, one-stop Web portal through which to
communicate medical information among different
Arab regions. The SBizPort and AMedPort were
developed to address these growing needs. In addition
to providing relevant information, the portals support
intelligence gathering and analysis, where intelligence is
defined as the product of acquisition, interpretation,
collation, assessment, and exploitation of information in
the respective domains [6]. The intelligence is presented
SBizPort
Scenario
The user searches for
Spanish Web pages
about electronic
commerce.
Search page The query “electronic
commerce” is used
(Fig. 1(a)).
Result page Approximately 40
results from 4 metasearchers (top 10
from each) are
displayed (Fig. 1(b)).
Categorizer The categorizer groups
retrieved Web pages into
20 folders, among which
are labeled “customs
agent,” “electronic
commerce,” and “foreign
commerce” (Fig. 1(c)).
Summarizer The summarizer
provides a 3-sentence
summary of the
circled result that
contains information on
an e-commerce event
held in 2000 in Caracas,
Venezuela (Fig. 1(d)).
Visualizer
The SOM visualizer
categorizes about 40
Web pages onto 2
regions labeled “foreign
commerce” and
“international
commerce”
and displays hyperlinks
on the right (Fig. 1(e)).
AMedPort
The user searches for
Arabic medical
information about
excretion.
The query “excretion” is
used (Fig. 2(a)).
Approximately 30 results
from 4 meta-searchers (top
10 or fewer from each) are
displayed (Fig. 2(b)).
Examples of the results
include “middle ear
infection” and “skin
symptoms of diabetes.”
Examples of categories
include “children's
education” and “sports.”
The user selects the seventh
category titled “special
education,” within which
he browses 2 Web pages
about “questions and
answers” (Fig. 2(c)).
The 3-sentence summary is
listed on the left while the
original page about
petrochemical information
is displayed on the right
(Fig. 2(d)).
The SOM visualizer
categorizes about 30 Web
pages onto 6 regions and
displays hyperlinks on the
right (Fig. 2(e)). Examples
of the regions include
“special education” and
“sports.”
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
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(a) Search page
The user types in “comercio
electronico” to search for BI
information about electronic
commerce in Spanishspeaking regions.
Search button
Visualize button
Categorize button
(c) Categorizer
(b) Result page
Retrieved pages are
categorized into folders
labeled by key phrases.
Click to summarize in
3 or 5 sentences
Retrieved pages’ titles
and abstracts are listed.
(d) Summarizer
The summary is listed on
the left while the original
page on the right.
The SOM visualizer categorizes
about 40 Web pages onto 2
regions and displays hyperlinks
on the right.
(e) Visualizer
Fig. 1. Screen shots of SBizPort.
3.2.1. Collection building and searching
Figs. 1(a), 1(b), 2(a), and 2(b) show the search and
result pages of the two portals. On the search page, a user
can input keywords and choose whether to search,
organize, or visualize the results. The user can input
multiple keywords separated by line breaks and can
choose among a number of carefully selected information
sources from the Spanish or Arab regions by checking the
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
(a) Search page
The user types in
“excretion” to search for
medical information about
the topic in Arab regions.
Button to visualize results
Button to search for results
Virtual Arabic
keyboard
Button to categorize results
(b) Result page
(c) Categorizer
Click to summarize
in 3 or 5 sentences
Retrieved pages are
categorized into folders
labeled by key phrases.
Retrieved pages’ titles
and abstracts are listed.
(e) Visualizer
(d) Summarizer
The summary is listed on
the left while the original
page on the right.
The SOM visualizer categorizes
about 30 Web pages onto 6
regions and displays hyperlinks
on the right.
Fig. 2. Screen shots of AMedPort.
boxes. The result page lists search results according to the
information sources selected by the user.
To provide high-quality information, we manually
analyzed the existing information sources in the two
domains. For the Spanish business domain, key
business categories such as e-commerce, international
business, and competitive intelligence were searched
to obtain seed URLs (translated into English), that
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
were used for domain spidering/collecting of Web
pages. More than 183 seed URLs were obtained. A
Web crawler then followed these URLs to collect
pages automatically. The pages were then automatically indexed and stored in our database. In addition
to domain spidering, we performed meta-spidering of
six major search engines (Yahoo ES, Ahijuna,
Conexcol, Ambdirecto, Auyantepui, and Teoma)
using queries translated from English queries that
previously had been used to build an English business
intelligence search portal [23]. We chose these search
engines because of their rich Spanish business
content. The Spanish business collection obtained
from this method contained more than 476,084 Web
pages covering more than 22 countries.
Similarly, our Arabic medical collection was built by
using 105 seed URLs collected from seven major search
engines (Google, Yahoo, AlltheWeb, Ajeeb, ArabVista,
AltaVista, and DMOZ) and by meta-spidering these URLs
using keywords from an Arabic medical glossary [16].
The results are then filtered depending on their number
and quality. The resulting Arabic medical collection
contained more than 220,000 Web pages covering more
than 22 countries.
Apart from searching its own database, the SBizPort
supports meta-searching two domain-specific databases
(SBizPort collection and AMBDirecto) and six Spanish
general search engines (Yahoo Español, Terra, Ahijuna,
Auyantepui, Bacan, and Ascinsa). The AMedPort
supports meta-searching three domain-specific databases (AMedPort, Sehha.com, and ArabMedmag.com)
and three Arabic general search engines (Ba7th.com,
ArabVista.com, and Ayna). These meta-search engines
were chosen because of their rich content and domainspecific coverage. A virtual keyboard provided for
AMedPort facilitates input (see Fig. 2(a)).
3.2.2. Summarizer
The SBizPort and AMedPort summarizers were modified from an English summarizer that uses sentenceselection heuristics to rank text segments [25]. These
heuristics strive to reduce redundancy of information in a
query-based summary [3]. The summarization takes place
in three main steps: (1) sentence evaluation, (2) segmentation or topic identification and (3) segment ranking and
extraction. First, a Web page to be summarized is fetched
from the remote server and parsed to extract its full text.
All sentences are extracted by identifying punctuation
serving as periods. Important information such as presence of cue phrases (e.g., “therefore,” “in summary” in
the respective languages), sentence lengths and positions
are also extracted for ranking the sentences. Second, we
1705
use the Text-Tiling algorithm [15] to analyze the Web
page and determine topic boundaries. A Jaccard similarity
function is used to compare the similarity of different
blocks of sentences. Third, we rank document segments
identified in the previous step according to the ranking
scores obtained in the first step and key sentences are
extracted as summary. The summarizer can summarize
Web pages flexibly, using three or five sentences. Users
can invoke it by clicking the number of sentences for
summarization under each result. Then, a new window is
activated (shown in Figs. 1(d) and 2(d)), that displays the
summary and the original Web page.
3.2.3. Categorizer
The SBizPort and AMedPort categorizers organize
the Web pages (related to the query shown on top) into
20 (or fewer) folders labeled by the key phrases appearing most frequently in the page summaries or titles
(see Figs. 1(c) and 2(c)). Each categorizer relies on a
phrase lexicon in the relevant language to extract
phrases from Web page summaries obtained from
meta-searching or searching our collections. To create
the lexicons, we collected a large number of Web pages
in the two domains. From each collection of pages, we
extracted meaningful phrases by using the mutual
information approach, a statistical method that identifies
significant patterns as meaningful phrases from a large
amount of text in any language [30]. The approach is an
iterative process of identifying significant lexical patterns by examining the frequencies of word co-occurrences in a large amount of text.
The mutual information (MI) algorithm is used in the
approach to compute how frequently a pattern appears
in the corpus, relative to its sub-patterns. Based on the
algorithm, the MI of a pattern c (MIc) can be found by
MIc ¼
fc
fleft þ fright −fc
where f stands for the frequency of a set of words.
Intuitively, MIc represents the probability of cooccurrence of pattern c, relative to its left sub-pattern
and right sub-pattern. Phrases with high MI are likely to
be extracted and used in automatic indexing. For
example, if the Spanish phrase “gerencia del conocimiento” (knowledge management) appears in the corpus
100 times, the left sub-pattern (gerencia del) appears 110
times and the right sub-pattern (del conocimiento) appears 105 times, then the mutual information (MI) for
the pattern “gerencia del conocimiento” is 100 / (110 +
105 − 100) = 0.87. In addition, we employed an updateable PAT-tree data structure developed in [30] that
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
supports online frequency update after removing
extracted patterns to facilitate subsequent extraction.
Repetitive removal of sub-patterns therefore is not
necessary. In addition, we used a stop word list and
manual filtering to refine the results obtained.
Using the approach, we extracted 19,417 phrases
from the SBizPort collection and 68,079 phrases from
the AMedPort collection. The categorizer then uses
these phrases to categorize the Web pages nonexclusively (see Figs. 1(c) and 2(c)).
3.2.4. Visualizer
The resulting portals also support visualization of Web
pages retrieved using a Kohonen self-organizing map
(SOM) algorithm [17] to categorize and place Web pages
onto a two-dimensional jigsaw map [23] (see Figs. 1(e)
and 2(e)). SOM is a neural networks algorithm that has
been used in image processing and pattern recognition
applications. When applied to automatic categorization
and visualization of Web pages, SOM assigns similar
pages to adjacent regions with each region labeled by the
most frequently occurring phrases extracted by the mutual
information approach described. The larger the size of a
region on the map, the more the Web pages are assigned to
it. Users can click on a region to see a list of pages on the
right and can open pages by clicking the link-embedded
titles.
3.2.5. Web directory
In addition, each of our portals provides a Web directory of the resources in its specific domain. Organized in a
hierarchical manner, the directory was built from a combination of human identification and meta-searching. The
Spanish business directory contains 295 categories and
the Arabic medical directory contains 232 categories.
Both have a depth of 5 levels.
3.3. Enhancements of the approach
We believe that the proposed approach offers benefits
and new enhancements in five aspects: (1) New integration of existing techniques: Although some of the
techniques used in the approach have been studied in
prior work, we have not found a comprehensive
approach that addresses the problem of information
quality on the Web and the need for Web searching in
languages used in widely separated geographic regions
(e.g., Spanish and Arabic). By integrating human analysis with existing techniques for text processing, our
approach was developed to alleviate information overload in searching and browsing Web content in nonEnglish languages. For example, we have customized
the Kohonen self-organizing map algorithm to the
Spanish business and Arabic medical domains to support dynamic visualization of Web pages. This integration of visualization technique has been enhanced
from our previous work [6,23] by considering languages
(Spanish and Arabic) used widely in a multitude of
geographic regions and by applying the technique to
non-English domains. To our knowledge, there has been
no previous attempt to integrate the technique into an
application similar to the portals described here. (2)
Collection building: Previous work on building Web
collections typically focuses on English content due to
the more abundant resources available. To deal with the
challenge of supporting non-English Web searching, our
proposed approach was used to build non-English Web
collections encompassing wide arrays of geographic
regions and content providers. For example, the SBizPort collection was built from spidering more than 183
Spanish business Web sites located in such regions as
Argentina, Bolivia, Central America, Chile, Colombia,
Ecuador, Spain, Mexico, Paraguay, Peru, Uruguay, and
Venezuela. The AMedPort collection covered Web
resources obtained from such regions as Saudi Arabia,
Bahrain, Lebanon, Tunisia, Kuwait, Egypt, United Arab
Emirates, Switzerland, United Kingdom, USA, Russia,
and Canada. While existing search engines in those
regions mainly provide regional services, the SBizPort
and AMedPort collections respectively serve the entire
communities that use Spanish and Arabic in Web
searching. The collections also represent new advances
over the English business collection built in [23] and the
lack of its own Web collection in [6]. (3) Language
processing: To extract meaningful phases as input for
the categorizer and visualizer, we used the mutual
information technique that considered the co-occurrence
of terms in a large corpus (see Section 3.2.3). Because
the approach used the probabilities of the terms
appearing in the corpus rather than their linguistic
patterns as the criterion for extraction, the technique was
statistics-based and hence different from linguistic
techniques used in previous research (e.g., [23]).
Comparing with our previous work [6] (in which the
system only served three closely-located geographic
regions (China, Taiwan, and Hong Kong)), we have
enhanced the performance of this technique by using a
large number of Web pages from different regions as our
corpus and by testing the technique in the two chosen
languages. (4) User interface customization: The user
interface interfaces of SBizPort and AMedPort were
specially designed to bring about the industry features
and to address the language-specific needs. For
example, AMedPort provides a virtual keyboard to
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
assist in the input of the right-to-left Arabic language.
The images in SBizPort user interface are related to
major industries in Latin America. (5) Application domains: This research has extended to domains such as
Arabic medicine and Spanish business that are less
explored in prior work. As the online populations in
these two languages will grow significantly (see
Section 1), this work thus helps system developers to
easily customize their development to the particular
language they consider. We believe that our approach
can help multinational organizations to search effectively for non-English information on the Web.
4. Evaluation methodology
In this section, we describe our methodology for
evaluating the usability of the Web portals developed
by our approach. Our evaluation objectives are: (1) to
study how the Web portals developed by our approach can assist searching and browsing of specialized domains on the Web; (2) to compare our portals
with existing search engines in order to understand
the effectiveness and efficiency of our portals; and (3)
to evaluate the information quality and user satisfaction achieved by using our portals.
To achieve objective (1), we invited human subjects
to use our portals to search and browse the Spanish
business or Arabic medical domains, two specialized
domains that do not have as much coverage on the Web
as their English counterparts. To achieve objective (2),
we selected BIWE and Ayna as benchmarks against
which to compare SBizPort and AMedPort because of
their comprehensive coverage and functionality. BIWE
(http://www.biwe.com/) is a major Spanish search
engine providing information for the Spanish-speaking
community. It also has a detailed Web directory for users
to browse topics in which they are interested. Compared
with other Spanish search engines, BIWE's services are
more comprehensive and target more closely to
Hispanics. As one of the most visited Arab Internet
hubs, Ayna (http://www.ayna.com/) serves Arabicspeaking people of the Middle East and North Africa.
Unlike many Arabic search engines, Ayna is more stable
and reliable that serves as a good benchmark to support
a fair comparison with AMedPort. To achieve objective
(3), we asked subjects to provide subjective rating and
comments on information quality and user satisfaction.
4.1. Experimental design
We designed scenario-based search and browse tasks
consistent with Text Retrieval Conference standards
1707
[37] to evaluate the performance of our Web portals. For
example, a scenario for testing SBizPort was “America
Online (AOL) in Latin America,” where a search task
was “When was AOL Latin America launched in the
United States?” and a browse task was “Find the URLs
of financial portals where you can find stock quotes on
America Online.” In a scenario for testing AMedPort
“Prevention and treatment of cancer,” a search task was
“Give the name of one vitamin that helps to prevent
cancer,” and a browse task was “Find articles about
healthy diet and cancer prevention.” To further validate
the relevance of tasks, before conducting the actual
experiment we did a pilot test with three subjects for
each portal.
We recruited 19 Spanish students and 11 Arab students as volunteer subjects to evaluate the performance
of the SBizPort and AMedPort. In each one-hour
experiment, we introduced two systems (our portal
and the benchmark system) to a subject and randomly
assigned different scenarios to evaluate the systems.
Each scenario contained two search tasks and one
browse task. To test the impact of the domain-specific
collection, we asked the subjects not to use the
collection in the first task when using our portal but to
use it in the second task. In the third task, we asked the
subjects to use the SOM visualizer when using our
portal and to use the available browse tools (e.g.,
hyperlinks, Web directory) when using the benchmark
search engine (see Table 4). Although we did not impose
any time limit on completing the tasks, we found that
each subject spent an average of three minutes to finish a
search task and eight minutes to finish a browse task.
The order in which the systems were used was randomly
assigned to avoid bias due to sequence of use.
After using a system, a subject filled in a post-session
questionnaire about his ratings and comments on the
system. The experimenter recorded all verbal comments
or behavioral observations that were later analyzed
using protocol analysis [9]. Upon finishing the study,
each subject also filled in a post-study questionnaire to
rate each system in terms of information quality and
overall satisfaction and to provide additional feedback.
Table 4
A summary of the experimental setup
System
Scenario
Task
Task type
1
First
2
Second
1 and 2
3
4 and 5
6
Search
Browse
Search
Browse
The systems and scenarios were randomly assigned to subjects.
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
The questionnaire was developed based on the user
satisfaction measures used in [8,19]. We asked the
subjects to rate their satisfaction on each system along a
seven-point Likert scale.
To measure information quality, we modified the
16-dimension construct developed in [38] by dropping
the “security” dimension which is not relevant because
the information provided by the systems is already
public. To accommodate the different levels of importance in the remaining 15 dimensions, we invited two
experts to provide ratings on the relative importance of
different dimensions in the two domains (see Table 5).
The Spanish business expert is a senior executive of a
management consulting company in Mexico. Being a
native Spanish speaker, he had 24 years of experience in
business development, raising capital, negotiations,
finance, and strategic planning. He also had worked as
the Vice President of Business Development for the
Gallup Organization in Mexico. The Arabic medical
expert is an Arab microbiology Ph.D. student at a major
research university in the United States. These experts
provided answers that we used to judge subjects' performances in the tasks.
The subjects also provided demographic information,
which was kept confidential in accordance with the
Institutional Review Board Guidebook [31].
4.2. Hypothesis testing
Because the Web portals developed by our approach
encompassed Web resources from different Spanish or
Arab regions, we believed that they would provide
richer content and higher usability than those of
benchmark systems. Users could thus find relevant
results more quickly from our portals. With respect to
the two domains, we tested the following five sets of
hypotheses, none of which had been explored in
previous research.
H1. Using a domain-specific collection in SBizPort/
AMedPort enables users to achieve higher effectiveness
and efficiency than performing search tasks without its
support.
H2. SBizPort/AMedPort enables users to achieve
higher effectiveness and efficiency than relying on
benchmark search engines for searching.
H3. The use of SOM visualizer in SBizPort/AMedPort
enables users to achieve higher effectiveness and
efficiency than using benchmark search engines to
perform browse tasks.
H4. SBizPort/AMedPort users achieve a higher overall
satisfaction than users of a benchmark search engine.
Table 5
Definitions of 15 dimensions of information quality and expert ratings
Dimension
Expert ratinga
Definition
Spanish Arab
Presentation quality and clarity
Accessibility
The extent to which information is
Concise representation The extent to which information is
Consistent
The extent to which information is
representation
Ease of manipulation The extent to which information is
Interpretability
The extent to which information is
definitions are clear
Coverage and reliability
Appropriate amount of The
information
Believability
The
Completeness
The
Free-of-error
The
Objectivity
The
3
3
3
3
3
3
easy to manipulate and apply to different tasks
in appropriate languages, symbols, and units, and the
3
2
2
3
2
3
regarded as true and credible
2
not missing and is of sufficient breadth and depth for the task at hand 3
correct and reliable
2
unbiased, unprejudiced, and impartial
2
2
3
3
3
applicable and helpful for the task at hand
highly regarded in terms of its source or content
sufficiently up-to-date for the task at hand
easily comprehended
beneficial and provides advantages from its use
3
3
3
2
3
extent to which the volume of information is appropriate for the task at hand
extent to which information is
extent to which information is
extent to which information is
extent to which information is
Usability and analysis quality
Relevancy
The extent to which information is
Reputation
The extent to which information is
Timeliness
The extent to which information is
Understandability
The extent to which information is
Value-added
The extent to which information is
a
available, or easily and quickly retrievable
compactly represented
presented in the same format
Expert rating: 3 = extremely important, 2 = very important, 1 = important.
3
3
3
3
3
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
H5. SBizPort/AMedPort provides higher information
quality than a benchmark search engine.
To test H1, we compared the performances of using
(task 2) and not using (task 1) our domain-specific
collections. To test H2, we compared the search performances of our portal and the benchmark search
engine. To test H3, we compared browse performances
of using our portal's SOM visualizer and the benchmark
search engine's browse support tools. Because a
previous research [7] has conducted a focused evaluation
on the use of summarizer and categorizer to support Web
searching and browsing, we did not repeat the evaluation
of these tools here. To test H4 and H5, we compared
subjects' ratings on the aforementioned aspects. As each
subject was asked to perform similar tasks using the two
systems, we used a one-factor repeated-measures design,
which gives greater precision than designs that employ
only between-subjects factors [27].
4.3. Performance measure
We recorded the time the subject spent on each task
to measure the efficiency of using a system. We also
measured the effectiveness of using a system by the
following formulae:
Accuracy ¼
Number of correctly answered parts
Total number of parts
Precision ¼
Number of relevant URLs identified by the subject
Number of all URLs identified by the subject
Recall ¼
Number of relevant URLs identified by the subject
Number of relevant URLs identified by the expert
F value ¼
2 Recall Precision
Recall þ Precision
Accuracy reflects how well a system finds correct
answers for search tasks. To measure the browse task
performance, we used precision, recall, and F value.
Precision reflected how well the portal helped users find
relevant results and avoid irrelevant results. Recall
reflected how well the portal helped users find all the
relevant results that had been identified by experts. F
value was used to balance recall and precision
simultaneously [34], reflecting the performances
achieved by the expert and by subjects.
5. Experimental results and discussions
In this section, we report and discuss the results of
our user evaluation study. Table 6 summarizes the
1709
means and standard deviations of various performance
measures. Table 7 shows the p-values and results of
testing various hypotheses. Table 8 summarizes subjects' demographic profiles.
5.1. SBizPort performance
5.1.1. Search performance
Using SBizPort's domain-specific collection achieved
higher mean accuracy and lower mean efficiency than not
using it. However, the differences were not significant.
The figures show that employing our domain-specific
collection resulted in performance comparable to that
achieved by using all the meta-search engines in
combination, suggesting the comprehensive nature of
our collection. We nevertheless believe that the SBizPort
collection should be further enhanced to provide more
comprehensive results in a shorter time, so H1 was not
confirmed.
Comparing our portal with the benchmark search
engine, we found that the mean accuracy of SBizPort
was significantly higher than that of BIWE, while there
was no significant difference between the efficiencies
achieved by the two systems. We believe that SBizPort's
ability to provide comprehensive, high-quality information from many sources helped users get accurate
results. However, the efficiency of SBizPort was not
significantly better than that of BIWE. Because
SBizPort is a research prototype, it lacks the professional operations of BIWE. Therefore, H2 was partially
confirmed.
5.1.2. Browse performance
We found that SBizPort achieved a higher mean
precision, recall, and F value than BIWE. However,
only the difference in F value was significant at a 5%
alpha-error level and the difference in recall was
significant at a 6% alpha-error level. The results show
that SBizPort's browse support tools and SOM visualizer could enable users to find more relevant results
than BIWE. However, there is still room for improvements in terms of efficiency and precision. Therefore,
H3 was partially confirmed.
5.1.3. User ratings and comments
Subjects rated SBizPort more favorably than BIWE
in terms of information quality and overall satisfaction
(see Table 6). The mean differences between the two
systems' ratings ranged from 0.6 to 1.5 and were all
significant at a 5% alpha-error level. Subjects were very
satisfied with SBizPort. We believe that several aspects
of SBizPort contributed to its good performance: the
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
Table 6
Means and standard deviations of different measures
Measure
SBizPort
Mean
b
Task 1
Search performance
Task 2
Search performanceb
Task 3
Browse performancee
c
Accuracy
Efficiencyd
Accuracy
Efficiencyd
Precision
Recall
F value
Efficiencyd
Information quality (overall)
– Presentation quality and clarity
– Coverage and reliability
– Usability and analysis quality
Overall satisfaction
a
0.87
131
0.95
134
0.87
0.21
0.78
288
2.1
2.3
2.2
1.98
1.8
BIWE
S.D.
Mean
0.33
43
0.23
59
0.29
0.14
0.38
63
0.66
0.78
0.63
0.76
0.76
0.55
149
0.55
151
0.86
0.13
0.48
285
2.9
2.9
3.0
2.9
3.1
a
AMedPort
S.D.
Mean
0.50
48
0.50
37
0.34
0.085
0.49
24
1.07
1.3
1.1
1.1
1.7
0.64
141
0.50
141
0.43
0.26
0.24
289
2.6
2.4
2.9
2.4
2.2
a
Ayna
S.D.
Meana
S.D.
0.50
45
0.50
45
0.37
0.21
0.23
26
1.1
1.0
1.3
1.2
1.3
0.23
146
0.18
174
0.27
0.12
0.11
300
4.7
4.5
5.0
4.6
4.9
0.41
37
0.40
19
0.41
0.18
0.21
24
1.0
1.2
0.87
1.2
1.8
a
The range of rating is from 1 to 7, with 1 being the best.
When using our portals, the subjects were asked not to use our domain-specific collection in task 1 but used it in task 2.
c
In task 1, the “SBizPort” or “AMedPort” column refers to using domain-specific collection and the right column (“Benchmark”) refers to not
using domain-specific collection.
d
Efficiency was measured by the time (in seconds) used.
e
In task 3, the subjects were asked to use the SOM visualizer when using our portals and could use all available browse tools when using the
benchmark search engines.
b
different functions and have a catalog.” Subject #s18
said that the browse tools “made it easy to view retrieved
data.” Regarding the search performance, fifteen subjects commented that SBizPort did a good job or has a
greater variety than the benchmark search engine. For
example, subject #s7 said: “(SBizPort) gives lots of
pages related to what I look for from different
countries.” Subject #s10 said “(SBizPort) looks with
more information and (is) able to provide in detail.”
However, five subjects complained about the low speed
of the system, especially when retrieving information
from many meta-searchers.
On the other hand, the subjects were unhappy with
BIWE's lack of relevance and clarity in searching and
browsing. For example, subject #s7 said that BIWE
“gives irrelevant pages (of) other countries I'm not
high-quality meta-searchers and domain-specific collection used in SBizPort, the useful browse support
tools, and the comprehensive content coverage. H4 and
H5 were confirmed.
The subjects provided many positive comments on
SBizPort's search and browse capabilities. Twelve
subjects agreed that SBizPort was very useful for
searching Spanish business information. For instance,
subject #s10 said that SBizPort “is very useful for
searching,” and “(the information) is clear.” Subject #s1
said “For specific topics (SBizPort) gave out specific
results, making the searches better than other search
engines.” The subjects also liked the browse support
tools provided by SBizPort. A majority of seventeen
subjects commented positively on it. For example,
subject #s6 said that SBizPort was “really nice to have
Table 7
p-values of testing various hypotheses (alpha error* = 0.05)
Comparison
SBizPort vs. BIWE
Hypothesis
Measure
Effectiveness
Efficiency
H1
H2
H3
Accuracy
Accuracy
Precision
Recall
F value
Satisfaction
Information quality (overall)
0.42
0.002 ⁎
0.89
0.06
0.035 ⁎
0.005 ⁎
0.009 ⁎
0.85
0.30
0.84
H4
H5
a
Efficiency was measured by the time (in seconds) used.
⁎ p values V 0.05.
AMedPort vs. Ayna
a
Effectiveness
Efficiency
0.54
0.046 ⁎
0.22
0.09
0.07
0.000⁎
0.000⁎
0.83
0.011 ⁎
0.31
Result
a
Not confirmed
Partially confirmed
Partially confirmed
Confirmed
Confirmed
W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
Table 8
Subjects' demographic profile
Demographic Spanish subjects
information
(total: 19)
Country of
origin
Education
Age range
Gender
Hours of
using
computer
per week
Arab subjects
(total: 11)
Mexico (12), USA (3),
Lebanon (7),
Panama (1), Puerto Rico (1), Morocco (1), Iraq (1),
Colombia (1), Peru (1)
Mauritania (1),
Jordan (1)
Undergraduate (13),
Undergraduate (3),
bachelor earned (2),
associate degree (1),
master earned (3),
bachelor earned (2),
doctorate earned (1)
master earned (5)
18–25 (14), 26–30 (2),
18–25 (6), 26–30 (3),
31–35 (2), 41–50 (1)
36–40 (1), 41–50 (1)
Female (10), male (9)
Female (3), male (8)
b5 (1), 5–10 (2), 10–15 (1), 5–10 (1), 10–15 (3),
15–20 (3), 20–25 (9),
15–20 (1), 20–25 (2),
30–35 (1), N40 (2)
25–30 (1), 30–35 (1),
N40 (2)
interested in.” Subject #s9 said that it was “timeconsuming” to use BIWE. Moreover, most users did not
like the presence of pop-up advertisements when using
BIWE. Nevertheless, six subjects said that BIWE was
useful for searching Spanish business information.
Three subjects commented that the system was easy to
use and fast.
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5.2.3. User ratings and comments
Similarly to SBizPort, AMedPort received significantly better ratings than the benchmark search engine
in terms of information quality and overall satisfaction.
The mean differences ranged from 2.1 to 2.8 and were
all significant at a 5% alpha-error level. We believe that
AMedPort's good performance can be attributed to its
high-quality meta-searchers and domain-specific collection and its useful browse support tools. H4 and H5
were confirmed.
Subjects' verbal comments show better satisfaction
with AMedPort than with Ayna. Nine (out of eleven)
subjects said that AMedPort was useful or provides more
topics and information. For instance, subject #a7 said
AMedPort was “helpful in cross-referencing information
from specific to general.” Subject #a5 said AMedPort
was “very useful because it does meta-searching.”
Subject #a2 said the AMedPort was “very easy to use
for Arabs.” In contrast, Ayna received many negative
comments from subjects because of its lack of relevant
results and confusing interface. For example, subject #a2
said that Ayna was “very clumsy, disorganized, (and)
very brief.” Subject #a8 said she “couldn't easily access
it” and subject #a9 said Ayna was “hard to use.”
5.3. Discussion
5.2. AMedPort performance
5.2.1. Search performance
Using AMedPort's collection resulted in higher
mean accuracy and efficiency than not using it.
However, similarly to SBizPort, the differences were
not significant. We believe that the AMedPort collection should be improved to provide more comprehensive results to users in a shorter time. H1 was not
confirmed.
Comparing our portal with the benchmark search
engine, we found that the mean accuracy and efficiency
of AMedPort were significantly higher than those of
Ayna. We believe that, like SBizPort, AMedPort
provided comprehensive, high-quality information
from many sources and helped users find correct results
in a shorter time. H2 was confirmed.
5.2.2. Browse performance
Contrary to our expectation, AMedPort achieved
performance comparable to that of Ayna, as shown by
insignificant differences in precision, recall, and F
value. Yet, at 7% and 10% alpha-error levels, AMedPort
achieved better F value and recall respectively. So
AMedPort needs further fine-tuning to be able to
achieve a better performance. H3 was not confirmed.
The encouraging results from our experiment demonstrate that the proposed approach is useful to support
non-English Web searching and browsing. Although we
applied the approach to building two portals in different
domains and languages, the experimental results are
surprisingly similar. We believe that this was because
similar procedures were used to develop the portals and
ensured high information quality, comprehensiveness in
content coverage, useful functionality, and user-friendly
interface. These important components help users who
need to search for information from widely scattered
regions in a language used by a multitude of countries
and places. The results may also imply applicability of
the proposed approach to building portals in other
domains and languages. Given that the Internet will
likely become more and more internationalized [29], the
proposed approach is expected to benefit a wide range of
domains and users.
Looking more closely into the findings, we observed
that the performance differences between the two Arabic
search engines are generally larger than those between the
two Spanish search engines. This may be due to the
relatively weaker Internet development in Arabic-speaking regions. However, as Arabic gains importance on the
Internet, we expect the demand for better searching and
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W. Chung et al. / Decision Support Systems 42 (2006) 1697–1714
browsing will grow significantly. Meanwhile, the performance of existing Spanish search engines is expected to
lag behind the rapidly-growing Hispanic and Latino
populations. Our proposed approach may possibly fill
some of the needs.
Compared with previous research (such as [7,23]), our
experimental findings provide insights to non-English
Web searching in languages that are used in widelyseparated regions. New empirical findings and developments are provided in this work. For example, this
research is the first attempt to use and to empirically study
the SOM visualizer in supporting non-English Web
searching. The Web collections provided by SBizPort
and AMedPort are also much larger and contain more
diverse regional information than the one developed in
[23]. Meta-searching and post-retrieval analysis are new
applications in Spanish and Arabic. While major languages like English and Chinese will still be important on
the Web, the notion of “multilingual Web” is expected to
draw attention from practitioners and researchers in the
future. And this research will likely shed light on some
system development and decision support issues for nonEnglish Web searching.
6. Conclusions and future directions
As non-English speakers increasingly use the Web to
seek information, there is a need for better support of
searching the Web across different regions. However,
support for Internet searching in non-English speaking
regions is much weaker than in English-speaking regions.
This research proposes a language-independent approach
to building Web search portals to support non-English
Web searching. Based on the approach, we developed two
portals, SBizPort and AMedPort, for the Spanish business
and Arabic medical domains, respectively. Experimental
results show that the two portals significantly outperformed the benchmark search engines in terms of search
accuracy and user ratings on information quality and
overall satisfaction. The two portals also achieved precision and recall comparable to those of benchmark search
engines. Subjects much preferred our portals to the
benchmark search engines in many types of usage. We
therefore conclude that the proposed approach is useful in
supporting non-English Web searching. This research
thus contributes to developing and validating a useful
approach to non-English Web searching and providing an
example of supporting Web searching in different nonEnglish domains.
This study was limited in several ways. Our two
research prototype portals have speed and stability that
are not as good as those of commercial search engines
like the chosen benchmarks. Several subjects complained about the slow responses of our systems. We also
have been limited by the scarcity of prior work on nonEnglish Web searching, which has prevented a more
comprehensive review of a topic that possibly would
offer better criteria for designing our approach. As for the
user study, we had difficulty in recruiting native speakers
as our subjects. Future work should consider expanding
the sample size to establish a higher statistical confidence
in the experimental results.
We are pursuing several directions to extend our
research. As the notion of a “multilingual Web” continues
to draw attentions, we are developing scalable techniques
to collect and analyze information in different languages
meaningfully to relate diverse content to produce intelligence. For instance, multinational corporations (MNCs)
typically provide Web site information in different
languages. Analyzing MNC's relationships with their
multinational stakeholders could help provide a holistic
picture of how they stand in the international arena. Other
domains that we will explore include Spanish medical and
Arabic business domains. The resulting business intelligence from stakeholders will serve to guide global development strategies. Another challenging area is the
digital archiving of multilingual data from heterogeneous
sources — often scattered in different regions. We will
investigate techniques and methods to facilitate such a
process and better support non-English Web searching.
Furthermore, we will develop and validate new visualization techniques to support browsing and comprehending massive multilingual information on the Web.
Acknowledgments
This research was partly supported by funding from
the National Science Foundation Knowledge Discovery
and Dissemination (KDD) program #9983304, June
2003–March 2004 and October 2003–March 2004 and
from the University Research Institute Grant Program of
the University of Texas at El Paso. We are grateful to our
project members and the experts and the student
subjects who participated in the user study.
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Wingyan Chung is Assistant Professor of
CIS in the Department of Information and
Decision Sciences at The University of
Texas at El Paso. He received his Ph.D. in
Management Information Systems from
The University of Arizona, and M.S. in
information and technology management
and BBA in business administration from
The Chinese University of Hong Kong. His
research interests include knowledge management, Web analysis and mining, data
and text mining, information visualization, and human-computer
interaction. He has published in leading journals such as Communications of the ACM, Journal of Management Information Systems, IEEE
Computer, International Journal of Human-Computer Studies, and
Decision Support Systems. Contact him at [email protected].
Alfonso A. Bonillas received his B.S. in
Systems Engineering at the University of
Arizona. His main interests are Web development, systems optimization, programming,
and database management. Contact him at
[email protected].
Guanpi (Greg) Lai is a doctoral student in the
Systems and Industrial Engineering (SIE)
Department at the University of Arizona. He
received his B.S. in Computer Science from
Tsinghua University, China and M.S. in
Industrial Engineering from the University
of Arizona. His research interests include
embedded systems’ tasks scheduling, intelligent control (automobile, home automation),
data mining, and data visualization. Contact
him at [email protected].
Wei Xi received her masters degree in
Management Information Systems from the
University of Arizona in 2004 and her B.A. in
English from Xi'an Foreign Languages University, China (1995). She joined AI lab in
Spring 2003. Her areas of interest include
Web programming and database management. Contact her at [email protected].
Hsinchun Chen is McClelland Professor of
MIS at the Eller College of the University of
Arizona and Andersen Consulting Professor
of the Year (1999). He received the Ph.D.
degree in Information Systems from New
York University in 1989, MBA in Finance
from SUNY-Buffalo in 1985, and BS in
Management Science from the National
Chiao-Tung University in Taiwan. He is
author/editor of 10 books and more than
130 journal articles covering intelligence
analysis, biomedical informatics, data/text/Web mining, digital library,
knowledge management, and Web computing. Contact him at
[email protected].