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: email@example.com
Tae Kyoung Ahn
The Korea Institute for International Economic Policy, Seoul, Korea. Email: firstname.lastname@example.org
Woo Kwon Chang
IFK, Myongji University, Seoul, Korea. Email: email@example.com
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
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
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.
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 (http://www.aiai.ed.ac.uk/ 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)
ontologies is difficult and so to evaluate them three WorkingPaper (seriesStat)
methods including comparative studies and usabilities DiscussionPaper (seriesStat)
will be employed. Outlook ()
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
Long Term Savings ….
general time deposits
Open Type Money Trusts.
Specified Money Trusts
Personal Pension Trusts
Off Shore Mutual Fund
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
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
http://www.imf.org/external/ftp http://purl.org/dc/ Leigh, Danie
http://www.imf.org/external/ftp http://purl.org/dc/ Exchange
elements/1.1/ Rate Pass-
/wp/2002/wp02204.pdf title Through in
http://www.imf.org/external/ftp http://purl.org/dc/ Serial
/wp/2002/wp02204.pdf type (1)
http://www.imf.org/external/ftp http://purl.org/dc/ WorkingPaper
/wp/2002/wp02204.pdf type (2)
http://www.imf.org/external/ftp http://www.daml.ri.cmu. IMF Figure 7. Search Screen
http://purl.org/dc/ 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
(www.oecd.org). 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
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
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
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