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					Implementing an Ontology-Based Knowledge Management System in the
Korean Financial Firm Environment

Hyun Hee Kim
Department of Library and Information Science, Myongji University, Seoul, Korea. Email:

Soo Young Rieh
School of Information, University of Michigan, Ann Arbor, MI 48109-1092. Email:

Tae Kyoung Ahn
The Korea Institute for International Economic Policy, Seoul, Korea. Email:

Woo Kwon Chang
IFK, Myongji University, Seoul, Korea. Email:

This paper reports one part of a larger research             is an essential asset for success and survival in an
project that designs and implements an                       increasingly competitive and global market. However,
ontology-based knowledge management system                   in most organizations KM applications have not to date
which makes knowledge assets intelligently                   proven largely successful, arguably because Knowledge
accessible to Korean financial firms. The                    Management (KM) systems have paid too much attention
research develops an ontology model consisting               to technical solutions and too little to the value of well-
of an information ontology, a competency                     organized knowledge within the system.
ontology, a product ontology, and a knowledge                     Most vital organizational knowledge can be seen as
map. This paper introduces the ontology model                residing, for instance, in the skills and memory of the
by illustrating the four components and reports              organizational members. Thus successful knowledge
on the implementation and evaluation of the                  management needs to consider not only technical but also
information ontology for the searching of web                social aspects. The time is apparently right for
resources. Based on the content analysis of                  information science researchers to investigate how to
eight international bank web sites, a pilot system           represent shared knowledge as well as how to create an
of information ontology for web resources                    organization culture that encourages knowledge sharing.
consisting of Publication, Project, Member,                       In this study, ontologies are employed for both
Person, and Organization was constructed. The                knowledge-sharing and semantic web searches. The
pilot system includes databases and ontology-                research goal of this research is threefold:
based search engines. To evaluate the pilot                        To design the four components of information
system,     a   comparative    experiment      was                  ontology, competency ontology, product ontology,
conducted in which the performance of an                            and knowledge map
ontology-based system was compared with that                       To implement a pilot system based on these
of web search engines. The results indicate                         proposed components
that the ontology-based system can be used not                     To evaluate the pilot system in terms of relevance,
only to improve precision but also to reduce                        customer satisfaction, and staff satisfaction.
search time.                                                      This paper is structured first to describe the model of
                                                             ontology-based knowledge management, second to
                                                             introduce a pilot system that implements the information
 Introduction                                                ontology for semantic web searches, and third to illustrate
                                                             the evaluation study of the pilot system.
     Knowledge Management (KM) can be defined as
any process or practice of creating, sharing, and applying   What is Ontology?
knowledge (explicit or implicit) wherever it resides for
better performance in organizations (Swan et al., 1999).          Ontology can be simply defined as a formal, explicit
There is a consensus among organizations that knowledge      specification of a shared conceptualization (Gruber, 1993).
However, ontology has been often employed without an           Network (SN), and a collection of lexical tools. As a
attempt at standard definitions; worse, it has been used       semi-formal ontology, the SN consisting of semantic
to refer to different things in different fields. In library   types and relationships is used to categorize MT concepts.
and information science, ontology has often meant              Another example resides in the study of Qin and Paling
glossaries, data, thesauri, taxonomies, and formal             (2001) that converted the controlled vocabulary of the
ontologies and inference. Thesauri, taxonomies and             Gateway to Educational Materials (GEM) into an
ontologies all have in common a method of relating terms       ontology. The demand to convert controlled vocabularies
in a controlled vocabulary via a semantic hierarchy and        into ontologies is due to the limited expressive power of
associative relationships (Soergel, 1999).                     controlled vocabulary and semantic-searching through the
    There are, however, some differences. A taxonomy           Internet and intranet.
is a semantic hierarchy in which information entities are          Of the studies done on ontology-based information
arranged by hierarchical relationships whereas a thesaurus     retrieval, Hyvonen, Styrman and Saarela (2002) built such
deals with the relationships between terms that are            a system in which images were annotated according to
structured taxonomically (Daconta, Obrst & Smith, 2003).       ontologies.     Their    system     offered    the   same
That is, a thesaurus is a combined form of taxonomy and        conceptualization to facilitate focused image retrieval
semantic relations of terms. A formal ontology means           using correct terminology.        Sure and Iosif (2002)
the complex semantics of concepts and the relations            compared two ontology-based search tools with a typical
among concepts, their properties, attributes, values,          keyword-based search tool in terms of search time,
constraints, and rules. More importantly, ontologies as        mistake-making, usefulness, and tool development and
expressed in an ontology representation language such as       maintenance. The first ontology-based search engine
Web Ontology Language (OWL) can add values to                  was used to obtain a picture of the information available
taxonomies or thesauri through deeper semantics                in the system knowledge base while the second one was
(Kwasnik, 1999; Qin & Paling 2001). According to               used to give users semantic context. The search results
Ding and Foo (2002), the concept of deeper semantics can       revealed that ontology-based tools were generally at least
imply deeper levels of hierarchy, enriched relationships       as good as the keyword-based tools and, to an extent,
between concepts, conjunction and disjunction of various       even superior.
concepts, and formulation of inference rules. Another
difference is that thesauri and taxonomies are intended for    Ontology-Based Knowledge Management
humans whereas ontologies can be used by software
agents for knowledge-processing as well as by humans for           Abou-Zeid (2003) proposed an ontology engineering
knowledge-sharing.                                             process model based on the premise that ontology
                                                               development is a special application of Nonaka’s
                                                               knowledge creation model. Nonaka’s model views the
Related Literature                                             ontology engineering process as a spiral in which
                                                               interaction between ontology stakeholders’ tacit
Ontology-Based      Information     Representation      and    knowledge and explicit knowledge is continuous and
Retrieval                                                      dynamic.
                                                                   Vasconcelos (2001) applied knowledge management
     Apparently some researchers do not distinguish            and knowledge engineering techniques to the design of
ontology-based information retrieval from thesaurus-           information systems, especially organizational memory.
based retrieval. The distinction between these two             For this purpose he used ontologies to design, implement,
seems, however, to be of importance. WorldNet                  and evaluate a Group Memory System.
(Fellbaum, 1998) is one example of a linguistic ontology;          Reimer et al. (2003) described two case studies
however, it can be considered as an online lexical             conducted by the Swiss Life Insurance Group to prove the
reference system rather than a formal ontology. With a         practical applicability and superiority of ontology-based
design inspired by current psycholinguistic theories of        knowledge management over classical approaches based
human lexical memory, WorldNet contains about 100,000          on text-retrieval technologies. The first case study, in
word meanings taxonomically organized.                         the domain of skills management, used manually-
    Unlike taxonomies and thesauri, ontologies provide         constructed ontologies on the subjects of skills, job
both better semantic representation and machine-               functions, and education. The system’s purpose was to
understandable representation of knowledge. Thus, a            support the identification of employees with particular
number of ontologies were constructed based upon               skills. The second case study aimed to improve content-
existing thesauri. One example is the Unified Medical          oriented access to passages of 1000-page documents
Language System (UMLS) developed by the National               through the use of an ontology consisting of 1500
Library Medicine (Kashyap & Borgida, 2003). The                concepts linked by 47,000 weighted semantic associations.
UMLS consists of Metathesaurus (MT), Semantic
     Minghong et al. (1999) proposed an ontology-based
competence system which facilitates the location of
appropriate contact persons for business tasks requiring
specific knowledge, experiences, or skills. Sure (2003)
presented a number of semantic web methods and
technologies and showed their applicability to practical
knowledge management in corporate intranets and in the
web. Quan, Huynh, and Karger (2003) investigated the
knowledge management system (Haystack) built on RDF
to create, organize, and visualize personal knowledge
such as e-mails, documents, tasks, contacts, meetings, and
other information.
     Even though previous studies on ontology-based
knowledge management have proven to be active and
productive, few studies have as yet discussed an
integrated ontology-based knowledge management
system focusing not only on searches of web resources             Figure 1. A Model of Ontology-Based Knowledge
and company documents but also on support for                                     Management
identifying employees with particular skills and
appropriate company products.
                                                              Information Ontology

Conceptual Framework                                              For the information ontology for web resources, an
                                                              international banking firm was selected as a sample
     The goal of this study is to design and implement an     domain. To design this ontology, content analysis was
ontology-based knowledge management system adapted            conducted on the web sites of eight international banks:
to the environment of Korean financial firms, most of         World Bank, ADB, OECD, EBRD, IFC, IMF, WTO, and
whose primary objective is to provide customers with          APEC. On content analysis completion, one interview
a full range of the most modern banking products and          was conducted with a domain expert in an economic
services     by implementing      the    latest   advances    research institute.
in information technology, by developing and enhancing
business processes, and by continuously improving             Competency Ontology
service quality. To achieve this objective, staff skill
management (competency), product development, and                 In designing the competency ontology, a preliminary
information management appear to be the most important        content analysis was conducted using ten Korean bank
concepts.                                                     web sites and documents.      Surveys and interviews with
      As shown in Figure 1, the proposed ontology-based       the employees of the financial firms are in progress: a
model includes three ontologies and an integrated search      total of about 60 questionnaires will be mailed to bank
engine: an information ontology, a competency ontology,       employees and interviews are in progress to identify the
a product ontology, and a knowledge map. The                  kinds of skills that employees need for their work tasks.
information ontology is used primarily to search both
web resources and financial firm documents. The               Product Ontology
competency ontology is designed to identify employees
with particular skills as well as to determine the kinds of       For product ontology, fifteen Korean bank web sites
skills required to conduct tasks. The product ontology is     and their product databases will be analyzed and
employed primarily to select products. Finally, the           compared. Once the analyses of websites are completed,
knowledge map is the search engine employed to                interviews will be conducted both with the financial firm
integrate multiple resources such as structural,              employees and clients.
unstructured, and expert databases.

Ontology Design Method                                        Ontology-Driven Modeling

    The following section describes the methods which         Ontology Design Process
serve as the bases for designing three ontologies.
                                                                 The ontology design process follows the widely-
                                                              accepted five-stage methodological approach (Uschold &
Gruninger, 1996; Vasconcelos, 2001): (1) identification of      ontology for company documents. The ontology for web
purpose and scope, (2) knowledge acquisition and                resource search contains five subontologies including
conceptualization, (3) integration and reuse of other           Publication, Project, Person, Member, and Organization
ontologies, (4) formal specification, and (5) evaluation        while the ontology for company document search
and documentation. The design steps for building                contains only Document subontology as presented in
ontologies are described below.                                 Figure 2.

1) Ontology Purpose and Scope
    This stage aims to define the purpose and scope of the
ontology, describing its use, its users, and the scope of the
ontology. The proposed ontologies are constructed to
allow employers, managers, clients, librarians, and
general users to search semantically for information.

2) Knowledge Acquisition and Conceptualization
    The process of acquiring knowledge from a given
domain is described in this stage. In the study, in order
to collect glossaries of concepts (classes) for the domains
content analyses are first performed on web sites and
company databases. Next, expert interviews and surveys
are conducted. Finally, domain concepts, instances,
relations, and properties are identified, presented, and
associated with domain terms.

3) Ontology Integration
    Other ontologies can be employed in the process of
building a new ontology.         To obtain a degree of
uniformity across ontologies, definitions from other                 Figure 2. Overview of Information Ontology
ontologies will be reused. Several ontologies including
the enterprise ontology ( project/         The relationship between two subontologies is
enterprise/enterprise/ontology.html) will be consulted in       presented by solid lines. For example, the Publication
the design of the three ontologies.                             subontology is related to the Project subontology because
                                                                some projects produce publications as the results of
4) Concept Description and Formal Specification                 research. The Document subontology can be connected to
    This stage proposes that ontologies should be formally      the Publication subontology when internal documents
represented using an ontology language. OWL is used to          are uploaded to web servers for the public.
codify three ontologies given that it is currently the best-        The Is-A relation of Figure 2 indicates that one
known ontology language for the semantic web. This              concept is a subclass of another, meaning that the
stage involves the formalization of each term and the           collection of Is-A arcs specifies a categorization hierarchy.
constraints used by the ontology. Terms are represented         Therefore, an Individual Member is a subclass of Member.
through classes, relations, functions, and instances.
                                                                  Table 1. The Overview of Publication Subontology
5) Evaluation and Documentation                                    Publication (org_name, pubCode, pubAuthor, pubTitle,
    Ontology validation and verification are accomplished                       pubLan, pubYear, pubResearch_topic,
through the application of a set of guidelines which look                       pubResearch_counrty_region, has_URL)
for incompleteness, inconsistencies, and redundancies                       Serial (pubFrequency)
(Gomez-Perez, 1995). However, direct evaluation of                               JournalArticle (journalTitle)
                                                                                 Statistics (code)
ontologies is difficult and so to evaluate them three                            WorkingPaper (seriesStat)
methods including comparative studies and usabilities                            DiscussionPaper (seriesStat)
will be employed.                                                                Outlook ()
                                                                          Article ()
Ontologies                                                                Conference (conferenceTitle)
1) Information Ontology                                                   UnofficialPublication ()
   The information ontology can be divided into two
categories: the ontology for web resources and the
   Table 1 presents the overview of Publication                 experience-of (competency relation) Dealing (application
subontology.      This subontology includes metadata            area) in Foreign Exchange (primitive competency) as
elements to enhance the representation and retrieval of         formal notation, has-experience-of (Foreign Exchange,
publication resources.        In this subontology, the          Dealing) is employed.
Publication class is a top-level class composed of nine
properties all of which are inherent to all of the subclasses   3) Product Ontology
of the Publication class such as Serial and
UnofficialPublication. The conferenceTitle property is             As shown in Table 2, the product ontology has the four
applied only to the Conference class.                           main classes of Deposits, Trust, Investment Trust, and
                                                                Loans. The Deposit class has nineteen subclasses, and
2) Competency Ontology                                          among them Installment Savings Deposits and Time
                                                                Deposits have their own subclasses. This product
   The competency ontology is designed based on Stader          ontology targets general clients, but customized products
and Macintosh’s (1999) competency taxonomy. This                are excluded in this ontology because of the difficulty of
taxonomy includes primitive competencies and                    fixing their classes as well as their properties.
application areas.    To design the competency ontology
adapted to the environment of Korean financial firms,                  Table 2. Overview of Product Ontology
some primitive competencies are deleted and new
primitive competencies added. Modifications are also                     Deposits
made in the application areas.                                             Demand Deposits
                                                                           MMDA: Money Market….
                                                                           Corporate Savings Deposits
                                                                           Installment Savings Deposits
                                                                             household installment deposits
                                                                           General Installment Deposits
                                                                           Scholarship Deposits
                                                                           Long Term Savings ….
                                                                           Time Deposits
                                                                             general time deposits
                                                                           Open Type Money Trusts.
                                                                           Specified Money Trusts
                                                                           Personal Pension Trusts
                                                                         Investment Trusts
                                                                           Beneficiary Certificates
                                                                           Off Shore Mutual Fund
                                                                           Auto Loans
                                                                           Loan Secured by Deposits …...

                                                                Ontologies in OWL

                                                                   The three ontologies of information, competency, and
                                                                product are represented in three OWL files. Here,
     Figure 3. Overview of Competency Ontology                  classes and properties are main components of OWL
                                                                language. A class defines a group of individuals that
   The main classes of Figure 3 are Competency and              belong together because they share certain properties.
Entity. The Competency allows the representation of             Classes can be organized in a specialization hierarchy
different levels of competency granularity through the          using SubClassOf.       The first example in Table 3
creation of subclasses of competencies, and the Entity          indicates that a Researcher is a subclass of Person.
describes the different application areas in which a                Properties are determined based on whether they
specific competency can be applied. The two hierarchies         relate individuals to individuals (ObjectProperties) or
are combined by a set of competency relations such as           individuals to datatypes (DatatypeProperties). In Table 3,
has-skill-of and has-experience-of to allow the                 Org_type as a property of the Organization class is
combination of terms between hierarchies. For example,          defined as an object property where the Org_type
to describe the specific domain expert who has-                 property ties an Organization to an OrganizationType.
  Table 3. Classes and Properties
                            <!--subClassOf –>
   <owl:Class rdf:ID="Researcher">
             <rdfs:subClassOf rdf:resource=="#Person"/>

                           <!-- ObjectProperty-->
   <owl:ObjectProperty rdf:ID="Org_type">
       <rdfs:domain rdf:resource="#Organization"/>
       <rdfs:range rdf:resource="#OrgnizationType"/>

Knowledge Map

    The knowledge map is a search engine that integrates
multiple resources such as structural, unstructured, and
expert databases. Based on the above-mentioned product
and information ontologies, the structural databases
include product, web resources, and internal documents.                Figure 5.   System Architecture
Conversely, the unstructured databases have codified
implicit knowledge to be stored in web pages or Lotus          The databases have two kinds of files: the ontology
Notes (Davenport & Prusak, 1998).                          files in OWL and the RDBMS files. OWL ontology files
    The expert databases are added in order to access      are employed to design tables and their attributes in the
experts with tacit knowledge and are designed based upon   RDBMS files. To better understand data structures, the
the competency ontology. Figure 4 gives the overview       Resource Description Framework (RDF) model is first
of the knowledge map.                                      constructed. Based on the RDF model, annotated web
                                                           resources are stored in RDF triple tables. The Windows
                                                           NT server is utilized as the system’s server.


                                                              The proposed system has two kinds of database: OWL
                                                           ontology files and RDBMS files.

                                                           1) OWL Ontology Files
                                                               Each ontology has an ontology file represented in the
                                                           OWL language, resulting in three OWL ontology files.
                                                           For example, the information ontology has six top-level
                                                           classes including Publication and Document. The full
                                                           version of the information ontology has 35 classes and 64

                                                           2) RDBMS Files
                                                               According to OWL ontology files, database, web
                                                           resources, and internal documents are annotated manually
         Figure 4. Overview of Knowledge Map               and then, based on the RDF model, their annotated data
                                                           are stored in the RDF triple tables of the RDBMS.
                                                           Figure 6 shows the RDF graph which indicates that there
System Architecture                                        is an article identified by the web site address shown in
                                                           Figure 6 as well as that this resource has five properties
                                                           with accompanying values.
    Figure 5 provides an overview of the system
architecture. The ontology-based module has two
components: databases and ontology-based search
                                                                           designed to solve such problems. For example, to allow
                                                                           users to do hierarchy searching in a Publication DBMS
                                                                           file, two fields for a document type were created. For
                                                                           first-level classes such as Serial only the first fields used
                                                                           for the first-level classes are to be searched. For second-
                                                                           level classes such as JournalArticle only the second fields
                                                                           used for the second-level classes are to be searched.

                                                                           System Implementation of                     Information
                                                                           Ontology for Web Resources

                                                                              A full range of pilot systems is under development.
                                                                           This section describes an example of an Information
                                                                           ontology search. Figure 7 shows the first screen of the
                                                                           ontology-based knowledge management system.

                    Figure 6. RDF Model

     Based on this RDF model, annotated resources are
 stored in the RDF triple table shown in Table 4.

 Table 4. RDF Triple Table
          Resource                       Property              Value    Leigh, Danie
    /wp/2002/wp02204.pdf                   creator    Exchange
                                       elements/1.1/        Rate Pass-
    /wp/2002/wp02204.pdf                     title          Through in
                                                            Turkey       Serial
    /wp/2002/wp02204.pdf                   type (1)   WorkingPaper
    /wp/2002/wp02204.pdf                   type (2)   http://www.daml.ri.cmu.       IMF                          Figure 7. Search Screen
    /wp/2002/wp02204.pdf          -cmu.daml/organization
                                   2002           After a selection of information ontology is made
                                        elements/1.1/                      from the left frame of Figure 8, the right frame shows a
    /wp/2002/wp02204.pdf                      date                         search mode for the information ontology. In the search
                                                                           mode, a class (category) is selected from a drop-down list
                                                                           and the select button then pressed, at which the system
 Ontology-Based Search Engine                                              responds by producing a set of properties applicable to
                                                                           that class. Applicable properties are inheritable; thus
   Based on the RDBMS files, computer programs are                         any properties that apply to an ancestor of the selected
 created to allow the conducting of ontology-based                         class are also included in the set. The following shows
 searches. MS SQL RDBMS has been here employed.                            how to select a class as well as how to search databases
 However, the RDBMS reveals some limitations in                            using an example.
 representing hierarchy relation between classes or                            After selection of the WorkingPaper class as a
 properties and in defining inference rules. Search                        category and “all of international organizations” as target
 programs and RDBMS records have therefore been                            organizations, the system responds by producing the
seven properties of the WorkingPaper class as shown in       System Evaluation of Information Ontology
Figure 8. The property list allows the issuing of a query.   for Web Resources
For this search, “economic effects of ageing” is selected
as a subject code, “English” as a language code, and “all”        A comparative experiment was conducted to evaluate
as a country/area code, after which the system displays      the information ontology for web resources. The
the search results. The subject code was constructed         performance of the ontology-based system was compared
based on the topic taxonomy of OECD home page                with that of web search engines in terms of relevance and
( Figure 9 shows the full record of            search time. Ten researchers from an economic research
Marcos’s paper upon the clicking of one short record.        institution were recruited in October, 2002, to conduct
By selecting a hyperlinked “Resource_URL” field, full-       experiments in the researchers’ offices except on two
text documents in PDF format can be accessed.                occasions when both of which were conducted in the
                                                             library. Before the participants conducted their searches,
                                                             they were provided by the experimenter with a half-hour
                                                             presentation about the system.
                                                                  The participants were given a list of twenty tasks and
                                                             asked to perform both on a search engine of their choice
                                                             and the ontology-based pilot system. The tasks included
                                                             answering questions about the locating of scholarly
                                                             literature, statistical data, conference information, news,
                                                             people searches, and project searches.
                                                                  The effectiveness of the pilot system was measured
                                                             with respect to relevance and completion time for each
                                                             task. Relevance was measured with respect to the
                                                             precision of the top 15 results for each task. For each 15
                                                             results, the researcher selected a score of relevance on a
                                                             0-5 scale in which 0=don’t know, 1=very irrelevant,
                                                             2=irrelevant, 3=neutral, 4=relevant, and 5=very relevant.
                                                             The average relevance scores from 10 participants for
     Figure 8. Properties of the WorkingPaper Class          each task were then calculated and compared across 20
                                                             tasks as shown in Figure 10.
                                                                  The difference in relevance scores between the two
                                                             systems appeared to be relatively higher when the
                                                             participants were looking for information about people
                                                             (Q1, Q10, Q16), conference information (Q6), image (Q8,
                                                             Q9, Q19), and news information (Q18) while the
                                                             difference was lower in the cases of scholarly literature
                                                             (Q5, Q7, Q12, Q13, Q17).
                                                                  The results also show that, overall, the average
                                                             relevance score of the ontology-based system (4.53) was
                                                             higher than that of web search engines (2.51). The
                                                             average search time for ontology-based searches and for
                                                             web searches was compared: overall 1.96 minutes were
                                                             taken when using the ontology-based system and 4.74
                                                             minutes were taken when using general web search
                                                                  As shown in Figure 11, the difference in search time
                                                             was greater when the participants were looking for
                                                             information about images (Q8), projects (Q11), people
      Figure 9. Result Screen (Publication)                  (Q16), and organization information (Q20) while the
                                                             difference was smaller for the tasks on scholarly literature
                                                             (Q7, Q13).
                 6 .0 0
                                        O n to l g y S ea rch                                                                                          n
                                                                                                                                                       I tern et S ea rch
                 5 .0 0

                 4 .0 0

                 3 .0 0

                 2 .0 0

                 1 .0 0

                 0 .0 0
                                            Q1     Q2   Q3       Q4        Q5        Q6    Q7      Q8    Q9    Q10         Q11     Q12    Q13   Q14    Q15     Q16         Q17    Q18   Q19   Q20

                                                 Figure 10.           Relevance Comparison of Ontology Search and Internet Search

                                                                                                Ontology Search                           Internet Search

                          search time/min

                                                        Q1      Q2    Q3        Q4    Q5   Q6     Q7    Q8    Q9     Q10     Q11    Q12   Q13   Q14   Q15    Q16     Q17    Q18   Q19   Q20

                                                                                                              q u e ri s

                                                 Figure 11. Search Time Comparison of Ontology Search and Internet Search

Conclusion                                                                                                                  taken for ontology-based searches was much shorter than
                                                                                                                            that taken for web searches.
      There are several issues in the information retrieval                                                                       The greatest difficulty in this ontology-based
area. One of them is that web search engines produce an                                                                     approach is the extra work necessitated by the annotation
excess of search results and another is that many retrieved                                                                 of web sites based on ontologies. Due to the expense of
sites are irrelevant. These problems are attributable to the                                                                annotating resources, it appears that certain domains such
fact that common information-retrieval techniques rely                                                                      as the business sector might be more appropriate than
either on specific encoding of available information or                                                                     others for ontology applications.
simple full-text analysis, both of which lead to limitations                                                                      The future of this project will entail implementing
such as ambiguity of word meanings and vocabulary                                                                           the following ontologies using the same methodology as
inconsistency between texts and users. To address the                                                                       information ontology for web resources: the information
limitations, this study designed and implemented the                                                                        ontology for financial firm documents, the competency
ontology-based web retrieval system in which ontologies                                                                     ontology, and the product ontology. That is, the three will
were utilized to add semantics to web pages for use in                                                                      be transformed into OWL ontology files and RDBMS
semantic web searches (Berners-Lee & Fischetti, 1999).                                                                      files. The RDBMS files will be constructed based on
      To evaluate the pilot system, the performance of an                                                                   OWL files and RDF models. Data will be extracted
ontology-based system was compared to that of web                                                                           from databases and web sites of financial firms and then
search engines. The results revealed that the average                                                                       input into records of tables.   The final pilot system will
relevance score of the ontology-based system was higher                                                                     feature these three ontology subsystems as well as the
than that of web search engines. The average search time                                                                    knowledge map for integrating multiple resources. The
                                                                                                                            system will be evaluated in terms of relevance, financial
firm employee satisfaction, and financial firm client             Minghong, L. et al. (1999). A competence knowledge base
satisfaction.                                                      system as part of the organizational memory XPS-99:
     The proposed information ontology was constructed             knowledge-based systems, survey and future directions.
using expert implicit knowledge as well as banking firm            Lecture Notes in Computer Science, 1570, 125-137.
databases. Thus, to make the proposed pilot system                Qin, J. & Paling, S. (2001). Converting a controlled vocabulary
update systematically, the environment such as                     into an ontology: the case of GEM. Information Research 6(2).
communities of practice will be needed to collect                  Retrieved April 24, 2004, from:
implicit knowledge from the financial firm employees and
clients. Additionally, deep annotation of annotating              Quan, D., Huynh, D., & Karger, D. R. (2003). Haystack: A
databases (Handschuh, Staab, & Volz, 2003) can be                  Platform for Authoring End User Semantic Web Applications.
utilized for the web databases of the pilot system.                Proceeding of The International Semantic Web Conference (pp.
                                                                   738 – 753).
                                                                  Reimer et al. (2003). Ontology-based knowledge management
ACKNOWLEDGEMENTS                                                   at work: the Swiss Life case studies, In Davies, J. et al (Eds),
                                                                   Towards the semantic web: Ontology-driven knowledge
                                                                   management (pp.197-218). West Sussex, England: John Wiley
  This work was supported by the Korea Research                    & Sons.
Foundation Grant (KRF-2002-005-B20006)
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