DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE

MANAGEMENT FRAMEWORK FOR PROFESSIONAL

VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE

SERVICE INDUSTRIES

Yuh-Jen Chen

Department of Accounting and Information Systems, National Kaohsiung First University of Science and Technology

Kaohsiung, Taiwan, China

Meng-Sheng Wu

Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, China

Wei-Kun Kuo

China Steel Corporation, Kaohsiung, Taiwan, China

Keywords: Knowledge-intensive service industries (KISI), Professional virtual community, Knowledge management,

Empirical knowledge.

Abstract: With the advent of service-oriented knowledge economy in the 21st century, knowledge-intensive service

industries (KISI) have become a trend nowadays for industrial development. In knowledge-intensive service

industries, enterprise activities have highly creativeness. By performing and achieving each enterprise

activity, the domain professional knowledge and experiences involving various ideas such as service

innovation or service value-added are employed. Therefore, it is most urgent task for implementing

knowledge management effectively, to quickly accumulate knowledge assets of enterprise and increase the

efficiency of knowledge-intensive service industries. Professional virtual community is an interactive

platform for enterprise experts to mutual creating and sharing empirical knowledge in knowledge-intensive

service industries. The platform has recorded high-volume rubbish information and empirical knowledge

during the expert discussion. Therefore, how to manage and share these useful contents of knowledge

discussion has become an important issue for empirical knowledge management in professional virtual

community. This study presents a systematic approach to developing a framework for empirical knowledge

management to support professional virtual community in knowledge-intensive service industries. The

approach presented in this study comprises three phases: (i) proposing an empirical knowledge management

model for professional virtual community, (ii) designing an empirical knowledge management system

framework for professional virtual community, and (iii) implementing an empirical knowledge management

system prototype for professional virtual community. Results of this study facilitate efforts within the

professional virtual community to extract, verify, store, and share empirical knowledge in order to

effectively assist knowledge-intensive service industries enhancing service innovative abilities and creating

the best services for customers' requirements.

1 INTRODUCTION

With the advent of service-oriented knowledge

economy in the 21st century, knowledge has become

an important source for an enterprise to promote its

competition advantage while service is a critical

value for enterprises to push the economic growth.

Thus, knowledge-intensive service industries (KISI)

have become a trend nowadays for industrial

development (Bryson and Rusten, 2005; Chen

2009).

Enterprise activities have highly creativeness in

Chen Y., Wu M. and Kuo W..

DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL VIRTUAL COMMUNITY IN KNOWLEDGE-

INTENSIVE SERVICE INDUSTRIES.

DOI: 10.5220/0003430200050014

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 5-14

ISBN: 978-989-8425-55-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

knowledge-intensive service industries. By

performing and accomplishing each enterprise

activity, the domain professional knowledge and

experiences involving various ideas such as service

innovation or service value-added are used.

Therefore, it is most urgent task for implementing

knowledge management effectively, to rapidly

accumulate knowledge assets of enterprise and

enhance the efficiency of knowledge-intensive

service industries.

Enterprise knowledge management can be

implemented as either a systematization strategy or a

personalization strategy (Hansen, Nohria, and

Tierney, 1999; Nonaka and Takeuchi 1995).

Systematization strategy is to manage explicit

knowledge and enhance the spread and distribution

of explicit knowledge through information systems.

Personalization strategy allows an expert to share

other experts’ own tacit knowledge (empirical

knowledge) by cooperating and communicating with

those experts. Meanwhile, tacit knowledge

symbolizes the value of an enterprise and is

generally hidden inside of personal mental models.

The inability to transfer tacit knowledge to

organizational knowledge (explicit knowledge)

would cause it to be disappeared while knowledge

workers leave their posts, ultimately losing

important intellectual assets for enterprises.

Professional virtual community is an interactive

platform for enterprise experts to mutual creating

and sharing empirical knowledge in knowledge-

intensive service industries (Pan and Leidner, 2003;

Wenger 1998). The platform has recorded high-

volume rubbish information and empirical

knowledge during the expert discussion. Therefore,

how to manage and share these useful contents of

knowledge discussion has become an important

issue for empirical knowledge management in

professional virtual community.

In recent years, the proposed researches for

virtual community are increasingly (Lin and Hsueh,

2006, Chang et al., 2008, Li and Wu, 2010).

However, these recent studies focused mainly on

managing and searching for explicit knowledge from

documents and information in virtual community.

They still do not have a completed solution for

managing and sharing empirical knowledge from

professional knowledge and experiences. Therefore,

experts' empirical knowledge requirements in

professional virtual community can not be satisfied,

and furthermore can not create services that would

meet customer's satisfaction.

Hence, this study develops a framework for

empirical knowledge management to support

professional virtual community in knowledge-

intensive service industries and effectively assist

knowledge-intensive service industries enhancing

service innovative abilities. To accomplish this

objective, the following tasks are performed: (i)

propose an empirical knowledge management model

for professional virtual community, (ii) design an

empirical knowledge management system

framework for professional virtual community, and

(iii) implement an empirical knowledge management

system prototype for professional virtual

community.

2 DESIGN OF EMPIRICAL

KNOWLEDGE MANAGEMENT

MODEL FOR PROFESSIONAL

VIRTUAL COMMUNITY

This section first defines the knowledge-intensive

service industry and analyzes its characteristics.

Then, empirical knowledge for professional virtual

community in knowledge-intensive service

industries is modeled. Based on the modeled

empirical knowledge, an empirical knowledge

management model is finally designed to pave the

way for system framework design.

2.1 Definition and Characteristics

Analysis for KISI

Knowledge-intensive service industries (KISI) are a

service value chain of higher knowledge contents,

which have been established by utilizing cooperation

modes as well as by combining sources from

science, engineering and academia. Knowledge-

intensive service industries use innovative

operational modes and technology application

techniques to pursue the innovations of product,

brand management, operation mode and service

through conducting technologies, internet,

professional knowledge, and services. Knowledge-

intensive service industries involve business

services, communication services, financial services,

educational services, legal consultation services,

distribution services, and health services.

Based on the definition of knowledge-intensive

service industry, it has the following characteristics:

(1) Knowledge-oriented: In knowledge-intensive

service industries, the performance and

accomplishment of each enterprise activity must

highly rely on the utilization of domain knowledge

and experience to ensure that their business models

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

would normally be operated. Finally, the customers’

satisfaction and the enterprise market

competitiveness are increased.

(2) Knowledge expertise: Knowledge-intensive

service industries are a service value chain of higher

knowledge contents. The establishment and supply

of these professional services are mostly based on

professional knowledge. Therefore, the knowledge

expertise must be checked in collecting knowledge

to create quality services for customers’

requirements.

(3) Knowledge innovation: Service innovation is the

goal for knowledge-intensive service industries. To

provide better services to customers, enterprises

generally promote their service innovative abilities

by knowledge innovation that relies on the exchange

and communication of empirical knowledge.

(4) Knowledge value-added: Collaboration is one of

the important strategies of increasing competitive

advantage for knowledge-intensive service

industries. Thus, the scope of knowledge

requirement has been extended from the “point”

mode in the past into the “plane” mode nowadays so

that more completed knowledge value-added

services can be provided to customers.

2.2 Empirical Knowledge Modeling for

Professional Virtual Community

Empirical knowledge in a professional virtual

community can be exchanged and shared effectively

through interaction among experts, and it involves

extensive knowledge range. Thus, the empirical

knowledge exchanged and shared among experts in

a professional virtual community is modeled, as

shown in Fig. 1. In understanding the empirical

knowledge, experts first focus on the discussion

topic for exchanging and sharing empirical

knowledge. Then, the definition, purpose, and

implementation steps for the topic are shared and

understood respectively. Meanwhile, experts can use

the function of document search in virtual

community to search for and refer to other related

definitions, purposes, and implementation steps for

the topic. From these reference documents, related

discussion topics can be identified. Moreover, the

published dates and publishers would be referred

while experts understand the topic definitions.

Figure 1: Empirical knowledge model for professional

virtual community.

2.3 Empirical Knowledge Management

Model Design for Professional

Virtual Community

Based on the above characteristics of knowledge-

intensive service industries and the empirical

knowledge model for professional virtual

community, the empirical knowledge management

model for professional virtual community is

designed. As shown in Fig. 2, the business model of

knowledge-intensive service industry must be

performed and achieved by knowledge workers

using empirical knowledge to satisfy customer

groups from different service activities. However, to

enhance customers’ satisfaction degree effectively,

knowledge workers generally use the professional

virtual community to mutually create and share each

other’s empirical knowledge to promote themselves

knowledge innovative abilities and create quality

services that meet customers’ satisfaction. In the

professional virtual community, knowledge

discussion contents during the expert discussion are

important sources for accumulating an enterprise’s

empirical knowledge. This precious empirical

knowledge can be managed and reused effectively

through knowledge extraction, verification, storage,

reasoning, adaptation, and expert recommendation

and consultation in order to achieve the goal of

DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL

VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE SERVICE INDUSTRIES

service innovation for knowledge-intensive service

industries.

Empirical Knowledge Management for

Professional Virtual Community

Knowledge-Intensive Service Industry (Site A)

Knowledge

Workers

Service

Activity

Service

Activity

Service

Activity

Customers

Customer

Requirements

Knowledge-Oriented

Business Model

Customers

Knowledge

Flow

Knowledge

Flow

Knowledge-Intensive Service Industry (Site N)

Knowledge

Workers

Service

Activity

Service

Activity

Service

Activity

Customers

Customer

Requirements

Customers

Knowledge

Flow

Knowledge

Flow

‧‧‧

Professional

Virtual

Community

Professional

Virtual

Community

Knowledge-Oriented

Business Model

Knowledge

Discussion Contents

Empirical

Knowledge

Extraction

Empirical

Knowledge

Verification

Empirical

Knowledge

Types

Knowledge

Workers

(Experts)

Expert

Recommendation

and Consultation

Empirical

Knowledge

Storage

Knowledge Innovation

by Empirical Knowledge Sharing

INTERNET

/INTRANT

Knowledge-Intensive

Service Industry (Site F)

Experts

Service

Innovation

Service

Innovation

Empirical

Knowledge

Reasoning and

Representation

Knowledge

Workers

Empirical

Knowledge

Adaptation

Know-

What

Know-

Why

Know-

How

Know-

When

Know-

Where

Know-

Who

Know-

With

Figure 2: Empirical knowledge management model for

professional virtual community.

3 DESIGN OF EMPIRICAL

KNOWLEDGE MANAGEMENT

SYSTEM FRAMEWORK FOR

PROFESSIONAL VIRTUAL

COMMUNITY

To effectively support the characteristics of

knowledge-intensive service industries and the

empirical knowledge management model for

professional virtual community as well as manage

the identified empirical knowledge from

professional virtual community in Section 2, this

section first analyzes the functional requirements for

empirical knowledge management system. Based on

these functional requirements, the agent-based

empirical knowledge management system

framework is then designed by utilizing software

agent technology.

3.1 Functional Requirements Analysis

The aim of empirical knowledge management

system is two-fold: (i) to manage the empirical

knowledge from each service activity in knowledge-

intensive service industries and (ii) to share the

empirical knowledge based on the knowledge

workers’ knowledge requirements, in order to

facilitate the knowledge innovation and the service

innovation in knowledge-intensive service

industries.

To accomplish the system objective above, the

functional requirements analysis is performed

through two phases of “system environment

management functions” and “system use functions”.

They are discussed follows.

(1) System environment management functions

(i) Service process configuration management:

Service process in knowledge-intensive service

industries is a mutual-participation process with

one leading enterprise and several allied

enterprises according to different service

requirements. Thus, the leading enterprise must

be able to perform service process

reconfiguration and system reconfiguration for

allied enterprises which are comprised with

different service requirements.

(ii) Distributed management: Distributed

collaboration has become one of the important

strategies for knowledge-intensive service

industries to enhance their competitive

advantage. To effectively support the distributed

collaborative environment, the abilities of remote

control, coordination, and communication should

be provided in exchanging and communicating

empirical knowledge.

(iii) Empirical knowledge maintenance: To

supply correct empirical knowledge to suitable

knowledge workers at right time, the system

must periodically maintain empirical knowledge

to ensure the correctness and completeness of

empirical knowledge.

(iv) Knowledge access control: Service

innovation in knowledge-intensive service

industries depends on the innovation of domain

knowledge, while the innovation of domain

knowledge thus relies on managing and sharing

knowledge. To assist knowledge workers in

sharing correct empirical knowledge at the right

time and place, the system needs to set different

knowledge access authorizations according to

different users’ authorities in order to protect the

confidentiality of empirical knowledge in sharing

empirical knowledge.

(2) System use functions

(i) Knowledge worker registration: Knowledge

workers are important sources of empirical

knowledge. Hence, knowledge workers must

perform the knowledge registration before they

enter into a professional virtual community and

post knowledge discussion contents. The way

can facilitate the expert recommendation for em-

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

pirical knowledge sharing.

(ii) Expert withdrawal: Service process in a

knowledge-intensive service industry is dynamic.

Experts in allied enterprises can flexibly choose

to join or withdraw from the collaborative

environment. In this situation, the function of

expert withdrawal must be considered to ensure

the time effectiveness of empirical knowledge

sharing.

(iii) Empirical knowledge extraction: In the

expert discussion in professional virtual

community, high-volume and precious empirical

knowledge hidden behind the knowledge

discussion contents should be effectively

extracted to be managed and shared.

(iv) Empirical knowledge verification:

According to the extracted empirical knowledge,

the logics of empirical knowledge should be

verified effectively before archiving such

valuable empirical knowledge into an enterprise

knowledge repository to ensure the correctness

of empirical knowledge and provide knowledge

workers with reliable knowledge decision

support.

(v) Empirical knowledge storage: After

verifying the empirical knowledge, it can be

represented by a structured knowledge

representation method and stored into an

enterprise knowledge repository for later reuse.

(vi) Empirical knowledge reasoning: To satisfy

knowledge workers’ empirical knowledge

requirements, knowledge workers can describe

and inquire the encountered problems in the

topic discussion to match and obtain related

empirical knowledge and solutions by using the

knowledge reasoning.

(vii) Empirical knowledge adaptation:

According to knowledge workers’ past usage

behaviors, the suitable empirical knowledge

ontology can be adapted to satisfy every

knowledge worker’s knowledge requirements,

and ultimately to enhance the reuse value of

knowledge.

(viii) Expert recommendation: Based on

knowledge workers’ knowledge requirements,

they can search for appropriate experts from the

enterprise knowledge repository for knowledge

consultation to increase the sharing effect of

empirical knowledge.

(ix) Empirical knowledge communication: As

recommended appropriate experts, knowledge

workers can consult and communicate the

empirical knowledge with these experts through

online video conference systems such as

VidiNOW, Co-Life, and NetMeeting.

3.2 Agent-based Empirical Knowledge

Management System Framework

Design

According to the system functional requirements

analyzed in Subsection 3.1, this section adopts the

concept of software agent technology (Danesh and

Jin 2001, Luck, Ashri, and D'Inverno, 2004) to

design an agent-based empirical knowledge

management system framework. As shown in Fig. 3,

the system environment management functions

include agents of system management, service

process, collaborative activity, empirical knowledge

maintenance, and knowledge access. Meanwhile,

each collaborative activity agent can collaboratively

perform and complete a service activity by one or

more personal knowledge management agents.

Through the empirical knowledge management

agent, each personal knowledge management agent

can use system use functions including knowledge

extraction agent, knowledge verification agent,

knowledge storage agent, and expert

recommendation agent to extract, verify, and store

empirical knowledge as well as recommend experts.

Based on the recommended experts, knowledge

workers can actually consult and communicate

empirical knowledge with these experts through the

knowledge communication agent. Furthermore, each

personal knowledge management agent can reason

and retrieve empirical knowledge through the

knowledge access agent and the empirical

knowledge reasoning agent. In reasoning and

retrieving the empirical knowledge, the empirical

knowledge adaptation agent can adapt a suitable

empirical knowledge ontology according to

knowledge workers’ use behaviors so that the

reasoned and retrieved empirical knowledge would

be better to satisfy knowledge workers’ knowledge

requirements. Finally, the knowledge worker

registration and withdrawal agent is provided to

knowledge.

4 MODELING OF EMPIRICAL

KNOWLEDGE MANAGEMENT

SYSTEM

System modeling defines system-level details in

terms of a set of models.

DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL

VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE SERVICE INDUSTRIES

Figure 3: The agent-based empirical knowledge

management system framework.

Two rationales are applied to system modeling.

First, to fully embrace and comply with industry

standard object models and architectures to enable

the interoperability with other empirical knowledge

management modules and a wide variety of other

software systems. Second, to employ industry best-

practice modeling techniques in a proposed

development process to facilitate the management of

system complexity.

As Unified Modeling Language (UML) (Booch,

Rumbaugh, and Jacobson, 1999; Jacobson,

Christerson, Jonsson, and Overgaard, 1992) has

emerged as the notational standard for object-

oriented modeling and is relatively comprehensive,

UML was utilized during the system modeling

phase. Using standard modeling techniques may

standardize and facilitate the development process

(Rational Unified Process, RUP) by using common

concepts, notations and supporting tools, and

consequently increase system compatibility with

other software systems.

System modeling can be classified into dynamic

aspect and static aspect. Meanwhile, the dynamic

aspect mainly describes system behavior (i.e.,

behavioral diagrams) which includes use case

diagram and sequence diagram. The static aspect

represents system structure (i.e. structural diagrams)

which includes class diagram and deployment

diagram. They are described below.

4.1 Behavioural Diagrams (Dynamic

Aspect)

4.1.1 Use Case Diagram

Use case diagram mainly describes the interactive

behavior between actor and system. Figure 4

presents the use case diagram of empirical

knowledge management system.

Figure 4: Use case diagram for empirical knowledge

management system.

4.1.2 Sequence Diagram

Sequence diagram describes the dynamic behaviors

of message transmission among objects in a system.

Figures 5 and 6 depict the sequence diagrams of

empirical knowledge reasoning and verification,

respectively.

Figure 5: Sequence diagram for the “Empirical

Knowledge Reasoning”.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

Figure 6: Sequence diagram for the “Empirical

Knowledge Verification”.

4.2 Structural Diagrams (Static Aspect)

4.2.1 Class Diagram

Class diagram presents the types of classes in a

system and the static relationships between classes.

Figure 7 shows the class diagram of empirical

knowledge management system.

Figure 7: Class diagram for empirical knowledge

management system.

4.2.2 Deployment Diagram

Deployment diagram represents the static structural

relationship between hard/software components in a

system and explains the details of its establishment.

As shown in Fig. 8, it displays the deployment

diagram of empirical knowledge management

system.

5 SYSTEM IMPLEMENTATION

According to the above results of system framework

design and system modeling, a prototype was

implemented to demonstrate the proposed empirical

knowledge management system for professional

virtual community. The implementation

environment and implementation results with an

illustrative example were explained below.

Figure 8: Deployment diagram for empirical knowledge

management system.

5.1 Implementation Environment

The implementation environment of this empirical

knowledge management system prototype for

professional virtual community was as follows:

‧PCs: Two Intel Pentium4-2.8G PCs.

‧Operation systems: MS Windows XP Professional

with Service Pack 3 and Linux Red Hat 9.0.

‧Web server: Tomcat 5.5 (Apache).

‧Web pages and core components: The Java Serve

Pages (JSP) programming language and Java Agent

Development Framework (JADE) were utilized to

build the web pages and work on agent

development.

‧Databases: My SQL 5.1.41 and Xindice XML

database.

‧Knowledge reasoning tools: Protégé 3.4.4 and

Pellet 1.5.2.

5.2 Implementation Results with a

Stock Investment Example

Based on the results of system modeling in Section

4, this section utilizes an example of stock

investment to explain the implementation of

functions of empirical knowledge verification and

DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL

VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE SERVICE INDUSTRIES

reasoning.

 Empirical Knowledge Verification: Figure 9

presents an empirical knowledge structure of stock

investment that has not yet been verified.

Meanwhile, the knowledge concepts “Share Issue”

and “Commercial Paper” are the sub-concepts of the

knowledge concept “Stock Exchange Market”, while

the knowledge concept “Government Bond” is the

sub-concept of the knowledge concept “OTC

Market”. By performing the knowledge verification,

the logic errors in the knowledge structure are

detected. Thus, the knowledge concept “Share

Issue” is modified as a sub-concept of the

knowledge concept “Corporate Bond” as well as the

knowledge concepts “Commercial Paper” and

“Government Bond” are modified as sub-concepts

of the knowledge concept “Stock”, as shown in Fig.

10. Figure 11 shows the OWL-based stock

investment knowledge representation before

verification and Fig. 12 shows the OWL-based stock

investment knowledge representation after

verification.

Figure 9: Stock investment knowledge before verification

(conceptual model).

Figure 10: Stock investment knowledge after verification

(conceptual model).

Table 1: The empirical knowledge reasoning rule.

OWL

Reasoning

Syntax

(?P rdf:type owl:TransitiveProperty)^

(?A ?P ?B)^(?B ?P ?C)→(?A ?P ?C)

Empirical

Knowledge

Reasoning

Result

(?Is_a rdf:type owl:TransitiveProperty)^

(?Stock ?Is_a ?Corporate Bond)^

(?Corporate Bond ?Is_a ?Ordinary Stock)→(?Stock ?Is_a

?Ordinary Stock)

NOTE: Parameter P denotes the transitive property. Parameters A,

B, and C represent the concept names of empirical knowledge.

Symbol “^” indicates the logical operator AND.

Figure 11: OWL-based stock investment knowledge

representation before verification.

Figure 12: OWL-based stock investment knowledge

representation after verification.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

 Empirical Knowledge Reasoning: Figure 13

displays a stock investment ontology constructed by

the ontology editor Protégé. According to the

defined reasoning rule in Table 1, the relationship

“Is_a” between knowledge concepts “Stock” and

“Ordinary Stock” can be reasoned, as shown in Fig.

14.

Figure 13: Stock investment knowledge before the rule

reasoning in Protégé.

Figure 14: Stock investment knowledge after the rule

reasoning in Protégé.

6 CONCLUSIONS AND FUTURE

WORKS

This study first defined the knowledge-intensive

service industry and analyzed its characteristics.

Then, the empirical knowledge from professional

virtual community was identified and modeled.

According to the definition, characteristics, and

empirical knowledge of knowledge-intensive service

industry, the empirical knowledge management

model for professional virtual community was

designed. Moreover, the software agent technology

was adopted to design the agent-based empirical

knowledge management system framework. Finally,

the empirical knowledge management system and

core techniques were implemented using UML

modeling techniques, object-oriented programming

language, and JADE. The primary results and

contributions of this study are summarized as

follows:

(1) Empirical knowledge management model for

professional virtual community: This study proposed

an empirical knowledge management model for

professional virtual community, which can be an

important reference model for implementing the

empirical knowledge management in other learning

organizations.

(2) Agent-based empirical knowledge management

system framework and core techniques: The

developed system framework and core techniques

can effectively extract, verify, store, reason, and

adapt the empirical knowledge from knowledge

discussion contents in professional virtual

community, to assist enterprises in fulfilling the

empirical knowledge management.

Results of this study facilitate the realization of

empirical knowledge management for knowledge-

intensive service industries to satisfy experts’

empirical knowledge requirements, and thus

promote service innovative capabilities, and

ultimately increase customers’ satisfaction as well as

enhance industrial competitiveness.

For the proposed core techniques in this study,

the following future research issues are

recommended: (i) In the process of empirical

knowledge verification, the knowledge structure is

validated and modified by experts. Thus, an artificial

intelligent-based method for automatic validation

and modification should be considered, and (ii)

Empirical knowledge is associated with situation

property. Situated empirical knowledge can

comprise basic elements such as spatial relation and

temporal relation. Therefore, future studies should

consider the situation property of empirical

knowledge to develop a method of situated empirical

knowledge representation and reasoning in order to

deduce a more suitable empirical knowledge that

would satisfy the requirements from a knowledge

requester under certain circumstances.

DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL

VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE SERVICE INDUSTRIES

ACKNOWLEDGEMENTS

The authors would like to thank the National

Science Council of the Republic of China, Taiwan,

for financially supporting this research under

Contract Nos. NSC98-2221-E-327-039 and NSC99-

2221-E-327-036.

REFERENCES

Booch, G., Rumbaugh, J., and Jacobson, I., 1999. The

unified modeling language user guide. MA: Addison-

Wesley.

Bryson, J. R. and Rusten, G., 2005. Spatial divisions of

expertise: knowledge intensive business service firms

and regional development in Norway. The Service

Industries Journal, 25(8), pp. 959-977.

Chang, C. K., Chen, G. D., and Li, L. Y., 2008.

Constructing a community of practice to improve

coursework activity. Computers and Education, 50(1),

pp. 235-247.

Chen Y. M., 2009. Determinants of industry performance:

region vs. country effects in knowledge-intensive

service industries. The Service Industries Journal,

29(3), pp. 297-316.

Danesh, M. R. and Jin, Y., 2001. An agent-based decision

network for concurrent engineering design.

Concurrent Engineering: Research and Applications,

9(1), pp. 34-47.

Hansen, M. T., Nohria, N., and Tierney, T., 1999. What's

your strategy for managing knowledge?, Harvard

Business Review, 77(2), pp. 106-116.

Jacobson, I., Christerson, M., Jonsson, P. M., and

Overgaard, G., 1992. Object oriented software

engineering: a use case driven approach. MA:

Addison-Wesley.

Li, N. and Wu, D. D., 2010. Using text mining and

sentiment analysis for online forums hotspot detection

and forecast. Decision Support Systems, 48(2), pp.

354-368.

Lin, F. R. and Hsueh, C. M., 2006. Knowledge map

creation and maintenance for virtual communities of

practice. Information Processing and Management,

42(2), pp. 551-568.

Luck, M., Ashri, R., and D'Inverno, M., 2004. Agent-

based software development. Boston: Artech House.

Nonaka, I. and Takeuchi, H., 1995. The knowledge-

creating company. Oxford University Press.

Pan, S. L. and Leidner, D. E., 2003. Bridging communities

of practice with information technology in pursuit of

global knowledge sharing. The Journal of Strategic

Information Systems, 12(1), pp. 71-88.

Wenger, E., 1998. Communities of practice, learning,

meaning, and identity. USA: Cambridge University

Press.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems