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Knowledge management system: an agent-

based approach

Chun-Che Huang and Gu-Hsin Lai

Laboratory of Intelligent Systems & Knowledge

Management, Department of InformationManagement, National Chi-Nan University, Pu-

Li, Nan-Tau, Taiwan, ROC

Correspondence: Chun-Che Huang, #1,University Road, Pu-Li, Nan-Tau, Taiwan,ROC

Received: 26 September 2003Accepted: 31 March 2004Published online: 17 June 2004

AbstractKnowledge is an important asset in any enterprise because of global

competition and the rapid development of information technology. Knowl-edge management (KM) is viewed as an important factor in improving the

competitive edge of an enterprise. By its very nature, knowledge is disparate

and heterogeneous and can be represented in various ways (text, pdf, html,etc.), and can be either structured or unstructured. It is, therefore, difficult to

acquire, organize or distribute knowledge using only traditional information

technology methods such as e-mail or file servers. Because of the autonomousand collaborative aspects inherent in agent-based technology, this may be a

possible solution to the problem. In this paper, an agent-based system is

proposed to conceptualize the activities of KM and an annotation process is

developed to address the heterogeneity issue of knowledge sources. Moreover,an agent conversation policy, which makes agent communication more

effective, is proposed. This agent-based system shows great promise in KM and

the conversation policy enhances communication between agents in aheterogeneous environment.

Knowledge Management Research & Practice (2004) 2, 80–94.

doi:10.1057/palgrave.kmrp.8500026Published online 17 June 2004

Keywords: Agent technology; Conversation policy; annotation

IntroductionAt the beginning of this new millennium, the emerging knowledgemanagement (KM) movement has come of age. Essentially, this movementis all about knowing about knowledge. More and more, KM is consideredthe main source of an organization’s competitive advantage (Hedlund &Nonaka, 1993; Grant, 1996; Prusak, 1996). KM was introduced into thebusiness world to help companies create, share, and use knowledgeeffectively. KM can be defined as a method to simplify and improve theprocess of creating, capturing, sharing, distributing, and understandingknowledge within a company (Khandelwal & Gottschalk, 2003). Wheneverknowledge becomes a key resource, it becomes critical to create and utilizenew knowledge continuously (Corby & Dieng, 1996). However, it isdifficult to have control over these key resources, due to the inherentcharacteristics such as invisibility, changeability and non-linearity, as wellas systemic, syntactic, structural and semantic levels of heterogeneity(Corby & Dieng, 1996; Sheth, 1998; Satyadas et al., 2001). Therefore, it isimportant for business to address the problem of how to transfer, manage,and share knowledge effectively in a heterogeneous environment.

Two main problems are involved in the deployment of KM. First, in mostorganizations, knowledge is distributed among many individuals, depart-ments, and data stores. It is difficult to access, share, and distributeknowledge gathered from different sources in a coordinated fashion.

Knowledge Management Research & Practice (2004) 2, 80–94

& 2004 Palgrave Macmillan Ltd. All rights reserved 1477–8238/04 $30.00

www.palgrave-journals.com/kmrp

Second, the heterogeneous issues of knowledge sourcesalso present a challenge in business. Information/knowl-edge is produced and dispersed daily throughout anorganization in the form of business or technicaldocuments, information manuals, legacy databases, e-mails, etc. Specifically, they are represented in variousformats (text, pdf, html, etc.) and can be structured, semi-structured or un-structured in different business.

To address these two problems, numerous agent-basedresearch related to KM has developed (Jennings &Wooldridge, 1996; Turban & Aronson, 1998; Shen et al.,2001). For example, Elofson et al. (1997) proposed acommunity of intelligent agents to facilitate knowledgesharing in an environmental scanning process. Wu(2001) proposed the use of software agents for knowledgemanagement, which focused on coordination of multi-agent supply chains and auctions. Aguire et al. (2001)proposed a multi-agent-based knowledge network. Rodaet al. (2003) proposed an agent-based system designed tosupport the adoption of knowledge sharing practiceswithin communities. However, none of these studiesfocuses on the heterogeneity problem inherent in knowl-edge sources and presents a completely agent-basedknowledge management (ABKM) system framework,including the acquiring and distributing of knowledgeboth actively and passively.

The three key issues in multi-agent systems arecommunication, cooperation, and coordination (Papazo-glou, 2001). Specifically, communication enables anagent to exchange messages and coordinate activities.Communication is crucial to allow cooperation andcoordination among agents to take place through aconversation in business. It is, therefore, the focus ofthis paper. Conversations are message sequences, invol-ving two or more agents, intended to satisfy a particularpurpose. As conversation is the most effective and directmeans of communication, conversation modeling isfundamental to the generation of collaborative activitiesin multi-agent systems. To simplify the design of agentconversation modules, a set of policies, called conversa-tion policies (CPs) (Lin et al., 2000), must be derived.

Greaves et al. (2000) discussed the role of conversationpolicies in agent communication, and proposed thatthese policies be broken down into several subtypes.Laurence & Hamilton (2000) proposed a mechanism thatdynamically combines conversation structures with se-parately established polices to generate conversations.Moore (2000) described a system for defining conversa-tion policies that allows conversation to exchangeexplicit representations of how they use messages to getthings done. Scott et al. (1991) proposed the use ofColored Petri Nets as a model underlying language forconversation specification. Lin & Douglas (2001) pro-posed a schema-based conversation policy to facilitatemulti-agent coordination and collaboration. None of thisresearch, however, provides well-defined messages tosupport knowledge exchange or considers the livelock/deadlock or effectiveness issues in communication.

To summarize, most agent-based research into thesubject of KM has focused on a single issue at a time,for example

� acquisition, organization, and distribution of knowl-edge (Fabiano & Cerri, 1996; Schwartz, 2000; Williams,2002)

� agent communication (Haddadi, 1996; Phillips & Link,2000; Biggers & Ioerger, 2001)

� agent-based systems in KM, without considerationbeing given to effective communication (Peng et al.,1999; Pierre et al., 2000).

Few studies have considered the problem of hetero-geneous knowledge sources, so important in ensuringeffective and reliable communication in ABKM systems.To address this heterogeneity and make the communica-tion between agents effective and reliable in business,this paper proposes an ABKM system. Our approachinvolves (i) an agent-based annotation process usingZachman’s framework (5W1 H) to model knowledge andto integrate the heterogeneity of knowledge documents,(ii) a well-defined and coordinated agent-based systemframework to capture and share knowledge resources thatare globally distributed effectively and (iii) a scheme-based agent conversation policy to improve the effec-tiveness of agent communication.

The remainder of this paper is organized as follows: Inthe first part, the agent-based annotation process isdescribed. In the second part, agent-based systems aredefined and the way in which the agents work is

Figure 1 Agent-based annotation process.

Knowledge management system Chun-Che Huang and Gu-Hsin Lai 81

Knowledge Management Research & Practice

illustrated. In third part, the conversation policy under aschema-based approach is developed. The system proto-type is presented. This paper closes with a conclusion.

Annotation for heterogeneity of knowledgesourcesTo solve the heterogeneity problem of knowledge sourcesin business, an annotation process is required to integrateheterogeneous sources using machine-understandableannotated metadata. The annotation process relates tothe unstructured formats of knowledge documents andallows knowledge sources to be accessed efficiently, suchthat these hetero-formatted or un-structured knowledgedocuments can be retrieved. The annotation includesthree types of description – basic description of annotatedknowledge (DB), descriptive information of annotation(DA), and relationship description (DR) among thedocuments, ontology, and generated knowledge.

DB includes general statements about the knowledgedocument and its source.

DA presents the 5W1 H (What, Where, Who, When,Why, and How) to represent annotated knowledge. Themachine-understandable annotated metadata in thispaper are based on Zachman’s framework (Inmon et al.,1997). The Zachman framework provides a systematicrepresentation for unstructured knowledge in organiza-tions. The use of the 5W1H model (i) allows therepresentation of knowledge to be consistent andflexible, and (ii) improves the sharing of knowledge in ameaningful manner over the web (Inmon et al., 1997;Huang & Kuo, 2003). The Zachman framework definesthe six dimensions of a problem as

� Entities (What? Things of interest).� Activities (How? The method).� Locations (Where? Places of interest).� Individuals (Who? Individuals and organizations of

interest).� Times (When? Things occur).� Motivations (Why? Reasons and rules).

5W1H is able to present these six dimensions inrelation to a particular event.

DR presents two types of relationship: (i) the relation-ship between the annotated knowledge document andother heterogeneous/unstructured documents and (ii)the relationship between the annotated knowledge andontology.

The proposed annotation process is presented inFigure 1. The knowledge documents in heterogeneousformats are annotated and stored as follows (Heflin &Hendler, 2000).

Step 0: Unstructured knowledge, which is presented invarious formats (text, pdf, html, etc.) is prepared as aninput to the annotation process.

Step 1: The annotation agent and the knowledgeableworker extract the knowledge contents from un-struc-tured or heterogeneous knowledge documents. Theannotation agent is responsible for inserting XML-description tags into the knowledge contents. The XML-based metadata for these knowledge contents are thengenerated.

Step 2: The annotation agent is responsible foridentifying the meta-data of knowledge contents as (DB,DA, DR) and for transforming them into XML formatdocuments. The annotated knowledge (KA), represented

<Basic description> <Name/> - The label assigned to the metadata subject <Identifier/> - The unique identifier assigned to the metadata subject <Version/> - The version of the metadata subject <Language/> - The language in which the metadata subject is specified <Ontology/> - A statement that clearly represents the concept and essential nature of the metadata <Obligation/> - Indicates if the metadata subject is required to always or sometimes be present <Comment/> - A remark concerning the application of the metadata subject <…….> </Basic description>

<Annotation description> <what/>- contents of the subject and event <who/>- something involved in subject and event <where/>- Locations of the subject <when/>- Time of the subject <why/>- motivations of subjects and reasons for event <how/>- Solution approach </Annotation description>

<Relation description> <Identifier> - The related unique identifier to the source <Type>-The relation type <Art_info>articulation info (if articulation process trigger) <……..> </Relation description>

DB

DA

DR

Figure 2 Annotation using XML tags.

Knowledge management system Chun-Che Huang and Gu-Hsin Lai82


by the 5W1 H model, is generated. Conception schema isalso created in this step. Conception schema is formed inan XML file, which describes the structure of theconcepts and properties of the annotated knowledge.

Step 3:

(1) The annotation agent stores the annotated knowl-edge document together with the conception schemain the metadata repository.

(2) The annotation agent registers the link (to the

original document) in the register.

The metadata repository validates the consistencybetween the elements in the benchmark ontology andthe metadata of the original source documents. Theregistry validates the consistency of the links between the

annotated knowledge document and the original knowl-edge source.

The format of annotated knowledge is exemplified byXML in Figure 2. Using the annotation process with theagent, the heterogeneity problem of knowledge sourcesin different formats and structure levels is solved sincethe (DB, DA, DR) of un-structured or semi-structureddocuments are annotated and become machine-readableand understandable by experts.

ABKM systemsIn this section, an ABKM system is presented.

The definition of agentsIn the ABKM system, agents are classified into three typesbased on the AOD model: ‘acquiring’, ‘organizing’, and

Figure 3 System architecture and knowledge acquisition and distribution processes.



‘distributing’ (Schwartz, 2000) and also into two interac-tion objects: ‘profiles’ and ‘external entities.’.

(1) Acquiring

(a) Collection agent (CA): Collecting data automati-cally from various data sources (e.g., database, datawarehouse, documents).

(b) Integration agent (IA): Organizing the data col-lected from the CA. The data may be in variousformats depending on the type of analysis techni-que (e.g. data mining, statistical analysis, OLAP,etc.).

(c) Analysis agent (AA): Using desired analysis tech-niques to analyze data, which are organized fromthe IA, to obtain analytical information.

(2) Organizing

(a) Annotation agent (AnA): This agent takes chargeof the annotation process.

(b) Knowledge storage agent (KSA): This is a kind ofknowledge repository where all the knowledge inelectronic business is stored.

(3) Distributing

(a) Delivery agent (DA): This agent has three func-tions: (1) to deliver analytical information to aparticular domain expert based on the informa-tion directed by Domain Expert Profile; (2) todeliver knowledge to a particular user based on theinformation directed by User Profiles; (3) to deliverknowledge to a particular KSA based on theinformation directed by Knowledge Storage Pro-file.

(b) Representation agent (RA): The main function ofthis agent is to appropriately represent the knowl-edge resulting from the analysis technique.

(c) User interface agent (UIA): The main function ofthis agent is to provide a friendly interface and toallow a user or domain expert to enter data/knowledge.

(4) Profiles

(a) User profile: This stores the information concern-ing the relationship between end users andspecific knowledge (e.g., Peter is interested inengineering knowledge).

(b) Domain expert profile: This stores the informationconcerning the relationship between the domainexpert and specific knowledge (e.g., Peter’s re-search area is IC design).

(c) Knowledge storage profile: This stores the infor-mation concerning the relationship between theKnowledge Storage Agent and the knowledge; e.g.,The knowledge about marketing is in the database,where ip¼163.22.22.123 and Databasename¼KM1).

(5) External Entities

(a) User (U): users within the electronic business.

(b) Domain expert (DE): experts within the electronicbusiness.

The architecture of the proposed systemBased on the agents and the interaction of the objectsdefined above, the agent-based system architecture, aswell as knowledge acquisition and distribution processes,is illustrated in Figure 3.

There are two different types of knowledge acquisitionprocesses: ‘acquire knowledge actively’ and ‘acquireknowledge passively’. For example, to acquire knowledgeactively:

(1) The AA sends a message to request the CA tocollect and analyze the data. The message requeststhe data required and the analysis technique to beused.

(2) The CA collects the data from the database, datawarehouse, or documents.

(3) The CA sends the collected data to IA and informsthe IA of the type of analysis technique used.

(4) The IA organizes the collected data based on theanalysis technique used and.12 then sends theorganized data to the AA.

(5) The AA analyzes the data and sends the results tothe DA.

(6) The DA requests the Domain Expert Profile contain-ing the information on the relationship betweenthe knowledge and domain expert.

(7) The Domain Expert Profile replies to the DA withthe information.

(8) The DA sends the analytical information and thedomain expert data to the RA.

(9) The RA sends the analytical information in anappropriate format, based on the analysis techni-que, to the domain expert.

(10) When the domain expert receives the information,some comments are added based on expert knowl-edge. The domain expert submits the expert knowl-edge to the UIA.

(11) The UIA sends the expert knowledge to the DA.(12) The DA sends the expert knowledge to the Knowl-

edge Storage Profile and requests the KnowledgeStorage Profile to obtain the information about therelationship between knowledge storage and expertknowledge.

(13) The Knowledge Storage Profile replies with theinformation to the DA

(14) The DA sends the un-structured or semi-structuredknowledge to the AnA to implement the annotationprocess, where annotated expert knowledge isproduced.

(15) The AnA sends the semi-structured annotatedexpert knowledge to the KSA based on the informa-tion sent by the Knowledge Storage Profile.

There are also two different types of knowledgedistribution processes: ‘distribute knowledge actively’



and ‘distribute knowledge passively’. Another example ofdistributing knowledge actively follows:

(1) After the 14th step in ‘acquire knowledge actively,’some annotated expert knowledge is produced andstored. The AnA sends the annotated knowledge tothe DA.

(2) The DA requests the User Profiles to gather informa-tion about which users need or are interested in thisknowledge.

(3) The User Profile replies to the DA with the informa-tion.

(4) The DA sends the annotated expert knowledge to theRA.

(5) The RA sends the annotated expert knowledge to theuser in an appropriate way based on the knowledgeformat and the relationship between the knowledgeand the user.

In this section, the roles of agents are clearly defined,and their working processes are also clarified. Thewell-defined roles and standardized working processesnot only support KM activities in electronic businessbut also let the agents know which agent shouldbe communicated with and what message will bereceived or sent.

This ABKM system characters are as follows:

� Intelligent: The agent automatically customizes itselfto the preferences of its user (or client), based onprevious experience and imprecise information frominteraction with users. The agent also automaticallyadapts to changes in its environment. In this system,intelligence of characters aids to construct the Profileand UIA. The Profile is updated as the environment orthe user’s preferences change.

� Autonomous: An agent is able to take the initiative andexercise a non-trivial degree of control over its ownactions through service agreements. In this system, theCA, AA, and IA handle the data/information and theAnA annotates expert knowledge autonomously with-out supervision of human being.

� Cooperation: An agent does not blindly obey com-mands, but makes suggestions to modify requests orask clarification questions in cooperation manner.

Table 1 TP in the ABKM system

Topics Description Arguments

Analysis_Process The process of analyzing data Analysis_method, Time_beg, Time_end

Get_Uprofile Getting information User Profile DBName, Analysis_Method

Get_Dprofile Getting information Domain Expert Profile DBName, Analysis_Method

Get_Kprofile Getting information Knowledge Store Profile DBName, Analysis_Method

Knowledge Getting expert knowledge from KSA Query_String, Knowledge

Get_Comment Getting comment from domain expert DEID (Domain Expert ID)

Send_Uknowledge Sending expert knowledge to user UID (User ID)

Submit_Query Submitting query operation to UIA Query_String

Group BehaviorsInteraction PatternsTask Constraints ACLMessage Protocols

ConversationSchemata

ColouredPetri Nets

"if-then"Rule Sets

Java ThreadClasses

Formulate

Specify

Convert

Verify

Figure 4 The steps of the schema-based conversation pro-

cesses.

Figure 5 Conversation process of data collection.

Schema

Name: Analysis_Data

Task: Analyze the data from various data sources

Topic: Analysis

Agent_types: AA, CA, IA

Acts: Request by AA, request by CA, inform by IA

Status: READY A, READY B, READY C, Request, Inform, WAITING A, WAITING B

&

Figure 6 The schema corresponding to the process of data

analysis.



Complex tasks are often carried out with incorporationwith other agents.

Agent communicationThe ABKM system is composed of different types ofagents. Each of them performs a single task. The

collaboration of agents is a necessary and important partof the schema. To facilitate multi-agent coordination andcollaboration, it is vital that agents exchange informationvia communication about goals, intentions, results, andstatus to other agents. One challenge is to ensure areliable and flexible communication heterogeneousagent in the sense that there are no possible incon-sistencies and deadlocks, conversations end with theexpected beliefs in the memory of each agent (Lin et al.,2000). This section presents a conversation policy ofagents to improve the efficiency of agent communicationand prevent from deadlock and livelock in agent com-munication. One of the conversation policy often used isschema-based conversation policy. The schema in sche-ma-based conversation policy is defined as ‘a conversa-tional pattern consisting of a set of conversation policiesfor information exchange among a group of agentcentering on a specific topic to accomplish a collectivework’ (Lin et al., 2000). In addition to schema, theschema-based conversation policy uses conversationmanager (CM) to make agent’s communication more

REQUEST_A

TIMEOUT A

READY A

READY B

READY C

WAITING A

REQUESTED_AB

SEND MESSAGES

REPLY C

TIMEOUT B

WAITING B

SENT_BC

RECEIVE MESSAGE

AA

CA

IA

Figure 7 The CPN, colors, and variables.

Rule 1 (for Request in A) If (Color(Ready_A,agt) = AA) then begin setValue(waiting, time) =5; setValue (requisted)=getValue(Ready_A); sendMessage (CA,Analysis_Method,Beg_Time,End_Time); end

Rule 2 (for Request in B) If (Color (Requested_AB,agt)=AA) then

begin setValue(waiting, time) =5;Source_Data=getData (Analysis_Method,Beg_Time,End_Time);

sendMessage (IA,Analysis_Method,Beg_Time,End_Time,Source_Data); end

Figure 8 The rule creation.

Area 1

I/OIE

EE

ANS

ASPSchemataLibrary

YP

Area 2

CM

Figure 9 The agent-based CM.



effective and efficient, and it can also uses Colored PetriNets to prevent agent’s communication from deadlock orlivelock. According to Lin & Douglas (2001), there arefour advantages to using a scheme-based conversationpolicy:

(1) It ensures the consistency and effectiveness of theagent conversation by considering sub-task con-straints.

(2) It reduces communication transactions by incorpor-

ating CMs that can quickly determine what partici-

pating agents should do, instead of having to resort to

lengthy reasoning.(3) It decreases implementation complexity by con-

structing CMs that separate the description of the

agents’ common functionality from that of commu-

nication and synchronization, to ensure local and

global coherency.(4) It enhances the reusability of software components.

Domain-independent and domain-specific conversa-

tion knowledge is organized and formulated into

hierarchies of conversation schema classes using

object-oriented methodologies.

For these reasons, a schema-based approach is used tospecify conversation policies in the ABKM system. Theschema-based conversation policy is a suitable approachto overcome the deadlock problems and make agent’scommunication more effective and efficient. The fivesteps of the schema-based conversation process ispresented in Figure 4 based on the work of Lin & Douglas(2001):

Step 1: Define the conversation topics.Step 2: Define the conversation schemata.Step 3: Use Colored Petri Nets (CPNs) to check if there is

deadlock or livelock. If deadlock occurs, identify it.Step 4: Create ‘If–then’ rules based on CPNs.Step 5: Generate java thread classes based on the ‘If-

then’ rulesThe detailed process is presented as follows:Step 1: The main target of the first step is to identify

conversation topics. A conversation typically focuseson one or more ‘topics’ each associated with task-relatedinformation. A topic can be described by a set of varia-bles that have values to be agreed upon by the agentsinvolved and have constraints that must be satisfiedby other agents or users. Conversation topics, denotedby TP, can be described by TP¼ (TP_ID, ARGUMENTS),

Figure 10 Adding the comments to the analytical information.



where TP_ID is the identification of a conversationtopic and ARGUMENT lists all arguments of that topic.Table 1 illustrates the partial TP of the proposed systemand more details can be referred in Huang (2004) andLai (2003).

Step 2: The second step identifies the schema. Figure 5illustrates the conversation process related to the datacollection process, (1) where the AA sends a message torequest the CA to collect and analyze the data;the message requests the data required and theanalysis technique to be used, (2) the CA collects thedata from the database, or documents from the datarepository unit, and (3) the CA sends the collected data tothe IA and informs the IA of the type of analysistechnique in use.

In Figure 6, the conversation scheme is definedaccording to the conversation process illustrated inFigure 8. In Figure 9, ‘Name’ represents the name of theschema and ‘Task’ describes the purpose of this conversa-

tion. In this example, the purpose of this conversation isto analyze the data from various data sources. The‘agent_type’ corresponds to the agents involved in thisconversation. In this example, three agents, AA, CA, andIA are involved in the conversation. ‘Status’ describes thestatus of the conversation.

Step 3: After defining the schema, it is described byCPNs. The CPNs provide a framework for the design,specification, validation, and verification of agent-basedsystems (Kristensen et al., 1998). In this paper, CPNs areused to check if there is deadlock or livelock in the ABKMsystem. The sub-schemata of the schema are representedas ‘transitions’ in the CPN. The ‘states’ of transitions aredescribed by ‘places’, in which ‘tokens’ correspond tostructured messages. The flows of relationships arerepresented as preconditions or post-conditions in theform of arc expressions.

The following steps present the construction process ofschemata (Lin & Douglas, 2001):

<?xml version='1.0' encoding='ISO-8859-1'?><!DOCTYPE rdf:RDF [

<!ENTITY rdf 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'> <!ENTITY NSCproject 'http://www.ncnu.edu.tw/im/iskmlab/ABKMproject#'> <!ENTITY rdfs 'http://www.w3.org/TR/1999/PR-rdf-schema-19990303#'>

<rdf:RDF xmlns:rdf="&rdf;" xmlns:ABKMproject="&ABKMproject;"xmlns:rdfs="&rdfs;"> ]><rdf:Description rdf:about=" ABKMproject _00060"

ABKMproject:Comment="marketing_q1_promotion.doc" ABKMproject:Identifier="1.0" ABKMproject:Language="Marketing" ABKMproject:Name="The source was made by MS Word2000" ABKMproject:Obligation="\\IASERVER\Marketing\MA000001025" ABKMproject:Ontology="English" ABKMproject:Version="department vision" rdfs:label=BD1>

<rdf:type rdf:resource="& ABKMproject;Basic description"/></rdf:Description> <rdf:Description rdf:about="& ABKMprojectVer00046" ABKMproject:ADid="AD1" rdfs:label="AD1"> <rdf:type rdf:resource="& ABKMproject;Annotation description"/>

< ABKMproject:what rdf:resource="& ABKMproject _00053"/>< ABKMproject:who rdf:resource="& ABKMproject _00054"/>< ABKMproject:where rdf:resource="& ABKMproject _00055"/>< ABKMproject:when rdf:resource="& ABKMproject _00056"/>< ABKMproject:why rdf:resource="& ABKMproject _00057"/>< ABKMproject:how rdf:resource="& ABKMproject _00058"/>

</rdf:Description> < ABKMproject:What rdf:about=" ABKMproject _00053" ABKMproject:whatcontent="To

promotion the Minolta D7i Digital Camera" rdfs:label=what#01 />< ABKMproject:Who rdf:about="& ABKMproject _00054" ABKMproject:whocontent="Aaron

Chen" rdfs:label=who#1 />< ABKMproject:Where rdf:about="& ABKMproject _00055"

ABKMproject:wherecontent="Taiwan" rdfs:label=where#1 />< ABKMproject:When rdf:about="& ABKMproject _00056"

ABKMproject:whencontent="2002/1/1~2003/1/1" rdfs:label=when#1 />ABKMproject:Why rdf:about="& ABKMproject _00057" ABKMproject:whycontent=" if the camera has more than 300 million pixels, then at least 128MB of memory is required " rdfs:label=why#1 />

< ABKMproject:How rdf:about="& ABKMproject _00058" ABKMproject:howcontent="Sell the Minolta D7i Digital Camera + 50 for free IBM Microdrive 340MB " rdfs:label=how#01 />

<rdf:Description rdf:about="& ABKMproject _00061" ABKMproject:Relate_Identifier="\\IASERVER\Marketing\MA000000852" ABKMproject:Type="Association"

ABKMproject:minsupport="0.6" ABKMproject: minconf ="0.7"

rdfs:label="R00061"> <rdf:type rdf:resource="& ABKMproject;Relation description"/>

</rdf:Description> </rdf:RDF>

Figure 11 Annotated XML format knowledge



Step 3.1: Identify agent types, attributes, and statevariables of the schema according to the topics.

Step 3.2: For every agent type, add the transitions forcommunicative acts or sub-schemata, and represent theactions performed by the same agent, which are alignedhorizontally.

Step 3.3: Add the places and flow expressions betweenthe transitions and connect them.

Step 3.4: Add the information exchange represented bythe places of the topics’ collective states occurring amongthe agents.

Step 3.5: Establish an external interface.Figure 7 illustrates the CPN, colors, and variables used

in the ‘Analysis_Data’ schema. The derived CPN repre-sentation of schemata allows verification for logicalconsistency, completeness and the presence of deadlockor livelock. The simulation technique can be used forverification (Cost et al., 2000).

Step 4: After verification, each conversation schema isconverted to a set of rules. Each ‘place(s)- transition’ ofindividual agents participating in the conversationin a CPN corresponds to the ‘condition’ part of arule. Every ‘transition-place(s)’ in a CPN correspondsto the ‘action’ part of a rule (Huang, 2004). Figure 8 is

an example of rule creation, where the rules initiateagents CA and IA to process their jobs while agent AAis ready.

Step 5: A set of java classes can be implemented basedon the rules created in step 4. When a set of java classes iscreated, the CM is formed based on these classes.

A CM, in a traditional agent communication approach,is a ‘point-to-point’, ‘multi-cast’ or ‘broadcast’ manager.Each communicates directly with the other. Based on thework of Lin & Douglas (2001), this paper proposes a novelagent conversation architecture called agent-based CMarchitecture. In this architecture, a group of agents worktogether in a cooperation area (see Figure 9). Each agentin a cooperation area routes all of its outgoing messagesthrough a local CM. All incoming messages are receivedfrom the CM as well.

The components of the agent-based CM architectureare defined as follows (Lin & Douglas, 2001):

IE (inference engine): This utilizes a load balancingmechanism, which allows a message to be forwarded toa new conversation.

ANS (agent naming sub-system): This is used to recognizethe process from the agent class to the agent instance, forthe registered agent stored in a yellow page (YP).

Figure 12 Passive knowledge acquisition.



ASP (active schema pool): This stores all acting schemata.It has a particular size called a ‘threshold,’ which is set forperformance based on certain criteria.

EE (CPN execution engine): The schema is executed by EE.Schemata library: This consists of a set of schema thread

classes, which comprise the template to construct aschema instance.

I/O: Message input/output module.In the agent-based CM architecture, the incoming

messages come from other CM(s). When the I/O modulereceives messages, it sends them to the IE. The IE detectsthe message if the size of the ASP reaches its threshold. Ifit is below the threshold, the CM selects an appropriateschema class from the schema library and creates aninstance of the class and adds it to the ASP. The EEanalyzes messages, recognizes the current situation andstates, creates the rules based on the schema and sends aninstruction to the appropriate agents about the topics.The topics are dependent on the current state.

Case studyThe XYZ Inc., a digital camera chain store, is currentlydeveloping a KM system, based on the proposed ABKM

solution, to facilitate the reuse of knowledge (to avoidtechnical terms, the KMS prototype is called in this case,the company). The KMS prototype presented in thissection describes the use of the aforementioned approachwith the objective of validating its feasibility in solvingthe heterogeneity problem and making communicationbetween agents effective and reliable.

PrototypeJADE (Java Agent Development Framework) is a softwareframework fully implemented in the Java language. Itsimplifies the implementation of multi-agent systemsthrough a middle-ware that claims to comply withFIPA specifications and through a set of tools thatsupports the debugging and deployment phase (Bellife-mine et al., 1990). The agent platform can be distributedacross machines (which do not even need to share thesame OS) and the configuration can be controlled via aremote GUI. Moving agents can even change theconfiguration at run-time from one machine to another,as and when required. JADE is completely imple-mented in Java language and requires at least the1.2 version of JAVA (the run time environment or

Figure 13 Distributing the knowledge automatically.



the JDK) or above. For these reasons, JADE is used inthe proposed system. The prototype system is presentedas follows:

(1) When the domain expert receives the analyticalinformation, some comments are added to makethe analytical information understandable to otherusers. In Figure 10, the analytical information isspecified using the ‘association-rule’ technique,where the minimum support is 0.6 and confidence is0.70. The data were collected from 2002/1/1 to 2003/1/1 in Taiwan. The user, Aaron, received the informa-tion and added his comments through the UIA inFigure 10. The information shows that customers inTaiwan always buy Minolta D7i and IBM 340MB with aminimum support of 0.6 and a minimum confidenceof 0.7. Aaron reads the analytical information andadds his comments through the web interface (UIA).Aaron can clip the link to see more detailed informa-tion. In this example, after Aaron reads the detailedinformation, he determines that ‘if the camera hasmore than 300 million pixels, then at least 128 MB ofmemory is required’. This ‘knowledge’ is annotated in

AnA and stored in KSA. The XML format of theannotated expert knowledge stored in KSA illustratedin Figure 11. The next time any salesman requiresknowledge relating to this camera product, he caneasily acquire it using this system.

(2) Figure 12 illustrates the process of acquiring knowl-edge passively. When the user, Aaron Chen discoversspecific knowledge, he sends it to the KSA throughthe UIA in Figure 12. In this example, Aaron Chenfound that customers who buy Pentax OptioS digitalcameras also buy an additional battery. He knew thatalmost all small-size digital cameras use non-standard(non-AA or AAA size) batteries. So he suggested thatwhen a customer buys a small-size digital camera heshould also buy an extra battery. Other salesmenwould also benefit by reminding customers that anadditional battery is needed. This ‘knowledge’ isannotated in AnA and stored in the KSA for sharingwith other salesmen.

(3) After the annotated expert knowledge is produced,it can be distributed actively. Figure 13 illustrateshow one receives the knowledge automaticallydistributed by the system. In this example, whenever

Figure 14 The knowledge query interface.



the knowledge relating to a ‘notebook’ is produced,the DA sends the knowledge to all who are inte-rested in ‘notebook’ knowledge (according to theUser Profiles). In this way, they obtain up-to-dateinformation.

(4) Figure 14 illustrates the distribution of knowledgepassively: a user attempts to acquire specific expertknowledge through UIA. In this system, users cansearch for knowledge according to 5W1H. In thisexample, the user wants information relating todigital cameras created by Aaron in Taiwan between2002/1/1 and 2002/12/1.

The query results received by users are presented inFigure 15. This system also presents the knowledge indifferent perspectives and dimensions, for example, adifferent expert’s viewpoint in a different location for adifferent time period. Figure 16 presents the knowledgefrom different ‘location’ viewpoints.

ConclusionsIn this paper, the agent-based KM system, ABKM isdeveloped. In the ABKM system, un-structured and

semi-structured knowledge sources are annotated asmachine-readable and understandable to humans,solving the heterogeneity problem. The roles ofagents are clearly defined, and their working processesare also clarified. The well-defined roles and standardizedworking processes of agents not only support KMactivities within electronic business, but also definethe messages through which agents communicate witheach other. The schema-based agent conversationpolicy and CM are used to decrease the communica-tion transactions over the network in electronicbusiness. They provide a reliable and effective agentcommunication mechanism.

The main contributions of this paper are: (1) a solutionto the heterogeneity problem of knowledge sources usingannotation, which was not a focus in previous studies, (2)a complete, rather than a partial, agent framework,defined and constructed, and (3) the creation of a CMbased on a schema-based conversation policy to avoidlivelock/deadlock and increase communication effective-ness for electronic business. This ABKM solution andcommunication approach shows great promise forknowledge sharing in a heterogeneous environment.

Figure 15 The results.



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