Future Generation Computer Systems 20 (2004) 61–79

Knowledge logistics in information grid environment

Alexander Smirnov∗, Mikhail Pashkin, Nikolai Chilov, Tatiana LevashovaSt. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences,

39, 14th Line BO, St. Petersburg 199178, Russia

Abstract

Rapidity of decision making process is an important factor for different areas of human life (business, healthcare, industry,military applications, etc.). Since responsible persons make decisions using available knowledge a delivery of necessary andtimely information for knowledge management systems is important. Knowledge logistics is a new direction of knowledgemanagement addressing this issue. It provides a set of activities for knowledge search, acquisition and integration fromdistributed sources located in information grid environment. The paper proposes a developed Knowledge Source Networkapproach (KSNet-approach) to knowledge logistics and its multi-agent architecture, and describes a research prototype of thesystem “KSNet” based on this approach.© 2003 Elsevier B.V. All rights reserved.

Keywords:Knowledge logistics; Intelligent systems; Ontologies; Multi-agent systems; Information grid

1. Introduction

Current trends of decision making in a wide rangeof applications require operating in information gridenvironment. This leads to an expansion of tools deal-ing with knowledge storing in the Internet and basedon intensive use of WWW-technologies and suchstandards as XML, RDF, DAML+ OIL, etc. [11,12].Thus, it is possible to speak about an evolution of theinformation environment, incorporating end-users andknowledge sources (KSs), from “regular” (with fixedinteractions between its elements) to “intelligent”(with flexible configuration of knowledge network inwhich humans are involved).

Grid is a modern technology for parallel dis-tributed system development that enables sharing,selection, and aggregation of resources distributedacross “multiple” administrative domains based on

∗ Corresponding author. Tel.:+7-812-328-8071;fax: +7-812-328-4450.E-mail address:smir@iias.spb.su (A. Smirnov).

their availability, capability, performance, cost, andusers’ quality-of-service requirements[41]. An in-formation grid environment consists of end-users(knowledge customers) and loosely coupled informa-tion/knowledge sources (experts, knowledge bases,repositories, documents, etc.). Growing importance ofintelligent support of the knowledge customers causesa need for acquisition, integration, and transfer of theright knowledge from distributed sources located ininformation grid environment. This knowledge has tobe delivered in the right context to the right person,in the right time for the right purpose. These activi-ties called Knowledge Logistics (KL) are required forglobal awareness, dynamic planning and global infor-mation exchange in information grid environment.

At present, there are number of projects based onthe grid technology. KL tightly correlates with theidea of semantic grid[30,50]. It is a new direction inthe knowledge management (KM) and enables evolv-ing of information grid into knowledge grid (a set ofwell-organized knowledge together with a set of KMoperations[48,49]).

0167-739X/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0167-739X(03)00165-1

62 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

Technology of knowledge fusion (KF) is basedon the synergistic use of knowledge from multipledistributed sources. It can be considered as a basisfor KL activities. The paper describes an approach toKL through KF called “Knowledge Source Network”(KSNet-approach) that allows to complement insuffi-cient knowledge and obtain new knowledge[38]. Thearchitecture developed for the KL system (called sys-tem “KSNet”) is based on this approach and utilizessuch technologies as ontology management, intelli-gent agents, constraint satisfaction, soft computing,and other[34].

The paper has the following structure: (i) it shortlydescribes the state-of-the-art of the KM areas, (ii)presents major KL technologies (KF operations andan ontology-driven methodology), a knowledge repos-itory structure and a multi-agent architecture of thesystem “KSNet”, and (iii) describes being developedresearch prototype of the system.

2. Knowledge management: state-of-the-art

KM is defined as a complex set of relations betweenpeople, processes, and technology bound togetherwith the cultural norms, like mentoring and knowl-edge sharing, which constitute an organization’ssocial capital[29]. KM includes the following ma-jor tasks: knowledge discovery (knowledge entry,capture of tacit knowledge, KF, etc.), knowledge rep-resentation (KB development, knowledge sharing andreuse, knowledge exchange, etc.), knowledge map-ping (identifying KSs, indexing knowledge, makingknowledge accessible)[8,18,43]. There are numberof different approaches proposed and tools developedfor these tasks solving based on the algorithms of datasearch and retrieval in large databases, technologies ofdata storage and representation, etc. Among them thefollowing ones can be pointed out: Microsoft Share-Point Portal [23], SearchServer/KnowledgeServer[19], Text-To-Onto[21], etc. (knowledge searchingand retrieving from different types of documents);Disciple-RKF [40], EXPECT [7], COGITO [10],OntoKick [39], etc. (knowledge acquisition from ex-perts and tacit knowledge revealing); OntoEdit[25],Protégé[28], Ontolingua[26], etc. (ontologies engi-neering); HPKB[27], etc. (KBs organization and de-velopment); KRAFT[44], InfoSleuth[24], RICA [4],

OBSERVER [22] etc. (knowledge and informationintegration). All these approaches do not provide awhole KL problem solution but solve particular tasks.

Possible applications of KL belong to the followingareas:

• Large-scale dynamic systems (enterprises) withdistributed operations in an uncertain and rapidlychanging environment, where the information col-lection, assimilation, integration, interpretation,and dissemination are needed[3].

• Focused logistics operations and/or web-enhancedlogistics operations addressing sustainment, trans-portation, and end-to-end rapid supply to the finaldestination. In this area the distributed informationmanagement and real-time information/knowledgefusion to support continuous information and know-ledge integration and exchange between all partici-pants of the operations are needed[13].Markets viapartnerships with different organizations, where thedynamic identification and analysis of informationsources and providing for interoperability betweenmarket participants (players) in a semantic mannerare needed[20].

For all the above areas management systems can bedescribed as an organizational combination of people,technologies, procedures and information/knowledge.

KL is based on individual user requirements, avail-able KSs, and content analysis in the informationgrid environment. Hence, systems operating in thisarea must react dynamically to unforeseen changesand unexpected user needs, keep up-to-date resourcevalue assessment data, support rapid execution ofcomplex operations, and deliver personalized resultsto the users/knowledge customers. Here proposedapproach to KL is based on the KF technology andtherefore assumes integration of knowledge from dif-ferent sources (probably, heterogeneous) into a com-bined resource in order to complement insufficientknowledge and obtain new knowledge.

3. KSNet-approach: major technologies

3.1. Knowledge Source Network

A network of loosely coupled sources located inthe information grid environment was called to as

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customer-oriented knowledge model versions at time instant t

customer-oriented knowledge models elements at time instant t ajt

t = t1 t = t2

s1

s5

s2

s6 s7 S1 s9

s10

S2

s11

s13 S3

s14

s15 S4

s12

S1, S4 knowledge sources models

knowledge sources models elements sl

a1t

s8 s3 s4

a3t

a2t a2t

a3t a1t

Fig. 1. Distributed multi-level knowledge fusion management as the KS network.

a “Knowledge Source Network” (KS network). Theterm “KS network” originates from the concept ofvirtual organization based on the synergistic use ofknowledge from multiple sources.Fig. 1 explainsroughly basic concepts of the KS networks and theirmulti-level configuration. The upper level representsa customer-oriented knowledge model based on a fu-sion of knowledge acquired from KS network units(KSs) that constitute the lower level and contain theirown knowledge models.

3.2. Knowledge fusion

In [16] the most complete sequence of main op-erations for KF referred to as knowledge chain wasproposed. It was used as a basis for developmentof KF process structure consisting of the followingoperations: (i) capturing knowledge from KSs andits translation into a form suitable for a supplemen-tary use, (ii) acquisition of knowledge from externalsources, (iii) selection of knowledge from internalsources (local KBs), (iv) knowledge generation: pro-ducing knowledge by discovering or deriving fromexisting knowledge, (v) internalization: changingsystem knowledge by saving acquired, selected, andgenerated knowledge, (vi) externalization: embedding

knowledge into system’s output for release it intothe environment, (vii) KF management: planning,coordination, collaboration, and control of operationsconstituting the KF process.

To increase the KF process rapidity it is necessarynot only to find required sources but also to identifytheir usefulness for solving a particular problem. Forthis purpose it is reasonable to: (i) utilize techniquesof knowledge/ontology reuse, (ii) perform indexationof stored knowledge, (iii) increase intelligibility ofknowledge representation for the users, and (iv) applyuser profiling.

3.3. Knowledge logistics system repository structure

One of the main components of any KM systemis a repository. Repositories have different structures,architectures and implementations depending on apurpose of a KM area the repository belongs to. In thedeveloped KSNet-approach repository (Fig. 2) is usedfor storage of information about different informationgrid elements (KSs characteristics and knowledgemodel, user interest models, domain descriptions,etc.) and relations between them. The following threecomponents in the repository structure were defined:(i) a semantic component is used for knowledge rep-

64 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

Knowledge map

User profile

Internal knowledge

base

Ontology Library

Semantic description component

Service

component

Physical component

Repository

Fig. 2. The system “KSNet” repository structure.

resentation in a common notation and terms; (ii) aservice component is used for knowledge indexingand search; (iii) a physical component is used forknowledge storage and reuse.

• The semantic componentcontains ontologies usedto describe the domain terms and correspondencebetween terms of different ontologies. An ontol-ogy is a formal description of entities and theirproperties, relationships, constraints, and behaviors[42]. It provides a common terminology that cap-tures key distinctions and is generic across manydomains, facilitating translation of concepts amongthese domains.

• The service componentcontains the followingelements:◦ A knowledge map including information about

locations of KS network units and informationabout alternative sources containing similar infor-mation and KSs characteristics. Monitoring toolsperform permanent checking of KSs availabilityand perform appropriate changes in the knowl-edge map. The knowledge map is meant to facil-itate and speed up the process of the KSs choice.

◦ A user profile including an organized storageof information about a user, his (her) requestshistory, etc. This element is used for a numberof purposes (faster search due to analyzing andutilizing request history and user preferences,Just-before-Time request processing, etc.).

• Thephysical componentcontains internal KB usedfor storage and verification of knowledge: (i) en-tered by experts, (ii) learnt from users (knowledgeconsumers), (iii) obtained as a result of the KFprocess, and (iv) acquired from KSs which are notfree, not easily accessible, etc.

3.4. Knowledge representation formalism

A formalism of object-oriented constraint networkshas been chosen for the ontology representation[31]. An abstract KS network model is based onthis formalism. This solution was mainly motivatedby such factors as support of declarative represen-tation, efficiency of dynamic constraint satisfaction,and problem modeling capability, maintainability,reusability, and extensibility of the object-orientedtechnology.

According to the chosen formalism an ontology (A)is defined as (Fig. 3): A = (O, Q, D, C):

O is a set ofobject classes(“classes”). Each of theentities in a class is considered as aninstanceof theclass. This set consists of two subsetsO = OI ∪ OII :OI = {o : ∃instance(o)} is a set ofnon-primitiveclasses, i.e. classes possessing a list of inherited at-tributes that specify necessary and sufficient condi-tions for definition of an instance as the class member,and OII = {o : ¬∃instance(o)} is a set ofprimitiveclasses, i.e. classes having only a set of attributes thatspecify the necessary condition only.Q is a set of class

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 65

Class A Class B

Superclasses

Class AB

Class AA

Class BA Subclasses

has_part relation (constraint)

uses associative relation (constraint)

Class C

Class CA

is_a relation (constraint)

Attributes

attribute to class accessory constraint

Attribute F

Attribute G

functional constraint

Domains

Domain K

Domain L

domain to attribute accessory constraint

compatibility constraint

Fig. 3. Object-oriented constraint network.

attributes (“attributes”).D is a set of attribute domains(“domains”).C is a set ofconstraints. For chosen no-tation the following six types of constraints have beendefined:C = CI ∪ CII ∪ CIII ∪ CIV ∪ CV ∪ CVI

.

• CI = {cI}, cI = (o, q), o ∈ O, q ∈ Q: belongingof attributes to classes;

• CII = {cII }, cII = (o, q, d), o ∈ O, q ∈ Q, d ∈ D:belonging of domains to attributes;

• CIII = {cIII }, cIII = ({o}, True ∨ False), |{o}| ≥2, o ∈ O: classes compatibility (compatibility struc-tural constraints);

• CIV = {cIV}, cIV = 〈o′, o′′, type〉, o’ ∈ O, o′′ ∈ O,o′ �= o”: hierarchical relationships (hierarchicalstructural constraints) “is a” defining class taxon-omy (type= 0), and “has part”/“part of” definingclass hierarchy (type= 1);

• CV = {cV }, cV = ({o}), |{o}| ≥ 2, o ∈ O: asso-ciative relationships (“one-level” structural con-straints);

• CVI = {cVI}, cVI = f({o}, {q}) → True ∨False, |{o}| ≥ 0, |{q}| ≥ 0, o ∈ O, q ∈ Q: func-tional constraints referring to the names of classesand attributes.

The most abstract class of the ontology (the topof the ontology’s taxonomy) is “Thing”. Classes,attributes, domains and constraints are considered asontology elements.

3.5. Ontology-driven methodology for knowledgelogistics

The system “KSNet” oriented to intelligent supportof its users (decision makers) applies ontologies foruser request processing. The following ontology typesfor the system were defined: (i) top-level ontologydescribing notation for ontology representation in thesystem; (ii) application ontology (AO) describing anapplication domain in terms of domain and tasks &methods ontologies; (iii) preliminary KS ontologydescribing KS in KS’s terms and the top-level on-tology notation; (iv) KS ontology (KSO) containingcorrespondence between terms of KS and AO; (v) pre-liminary request ontology describing user request inuser’s terms (which are used by the user for requestsinput) and the top-level ontology notation, (vi) requestontology (RO) containing correspondence betweenterms of preliminary request ontology and AO; (vii)domain ontology representing static knowledge abouta particular domain in terms of the domain; and (viii)tasks & methods ontology describing problem-solvingknowledge in terms of a domain or high-level termsthat are general for several domains. The ontologiesare stored in a common ontology library (OL) thatallows sharing and reusing them.

The OL’s ontologies share a common notation pro-vided by the top-level ontology. Domain ontologies

66 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

Application Ontology

Preliminary Knowledge Source

Ontology

Knowledge Source

Ontology

Tasks & Methods Ontology

Domain Ontology

Preliminary Request Ontology

Request Ontology

m:m

m:1 :m

m:m

1:m 1:m

m:1

m:1

Request

1:1

Knowledge Source

1:1

Legend:

− ontology

− "many-to-one" relationship

− associative relationship

1

Fig. 4. Relationships between ontologies in the ontology library.

and tasks & methods ontologies are formed as a newknowledge become available. The new knowledgehere is knowledge provided by experts, retrieved fromKSs, or obtained as results of user request processing.Both new ontologies can be created (if there is noontology relating to domain/task/method of the newknowledge) and existing ontologies can be expanded(otherwise). Relationships between the ontologies ofOL are presented inFig. 4.

Relationships between a domain ontology and atasks & methods ontology are established if knowl-edge of the domain are used by a task or a methoddescribed by the tasks & methods ontology. Accord-ing to the chosen formalism the tasks/methods ofthe tasks & methods ontology are described by on-tology classes, output and input parameters of thetasks/methods are described by attributes of theseclasses. If an attribute value from a domain ontologycan be considered as a parameter of a task/methodthen an associative relationship is established betweena domain ontology class holding the attribute and aclass of the tasks & methods ontology representingthe task/method and vice versa.

Parts of domain ontologies and tasks & methodsontologies make up an AO. AO is a conceptual modeldescribing a real-world application domain. It de-pends on a particular domain and problem. In casewhen a request has not been processed before partsof domain and tasks & methods ontologies relevantto the request are integrated into a new AO. Betweenclasses of ontologies parts of which were includedinto AO and appropriate classes of AO relationshipsare established. Otherwise, an existing AO is reused.RO is formed by alignment of preliminary RO andAO that is to be used for the request processing.Alignment is establishing links between two ontolo-gies via determination of a correspondence betweentheir elements. Thus, each preliminary RO is relatedto one AO and one RO (Fig. 4). AO reuse correspondsto the one-to-many relationship between AO and pre-liminary RO in the figure. The associative relationshipbetween RO and tasks & methods ontology illustratesthe case when methods resolving semantic conflicts(e.g., “class–attribute–attribute value” mismatch, con-flicts in classes subordination[9], etc.) between AOand preliminary RO are attached.

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The request processing assumes searching for KSs.The one-to-many relationship between AO and KSOis explained by the fact that several KSs can be foundfor a request. The same way as every request has itspreliminary RO, every KS is described by its prelim-inary KSO. The relationship one-to-many inFig. 4between preliminary KSO and KSO refers to by thefact that information from KSs relevant to the partic-ular request and correspondingly to the certain AO isused. Preliminary KSO, can, contain extra knowledgewith regard to such AO and request. Relevant to AOinformation is described by a part of preliminary KSOrepresented as a self-contained knowledge source on-tology. The associative relationship between tasks &methods ontology and KSO has the same meaning asin the case with RO.

A conceptual scheme of the user-oriented ontology-driven KL methodology is presented inFig. 5. Thesystem works in terms of a common vocabulary. Eachuser/user group works in terms of associated expand-able RO and thereby with a part of AO pertinent to theuser/user group. User profiles are used during interac-tions to provide for an efficient personalized service.Every user request consists of two parts: (i) structuralconstituent (containing the request terms and relationsbetween them), and (ii) parametric constituent (con-taining additional user-defined constraints). For therequest processing, an auxiliary KS network configu-ration is built defining when and what KSs are to beused for the request processing in the most efficient

Ontologies Library

User Request

Parametric Constituent

Structural Constituent

Request Ontology

User Constituent

Application Ontology

Constituent

Correspondence

User

Request Processing

Answer

Application Ontology

User L ogistics Manager

User Operational Manager User E ngineer

Request Ontology

Request Ontology

Knowledge Source

Passive Sources Databases, Knowledge Bases, Documents, etc.

Active Sources

Experts, Knowledge-based tools, etc.

Knowledge Source Ontology

Application Ontology

Constituent

Knowledge Source

Constituent

Correspondence

Instances

User

Profile User

Profile

User

Profile

Domain Ontology

Tasks & Methods Ontology

Knowledge

Map

Fig. 5. Conceptual scheme of the user-oriented ontology-driven knowledge logistics methodology.

way. For this purpose the knowledge map includinginformation about locations of KSs is used. Transla-tion between the system’s and KS’ notations & termsis performed using KSOs.

During KSO creation (when a new KS is attached tothe system) and modification (when an appropriate KSis changed), a correspondence between KS terms andthe AO terms is identified. As a result of this processa set of corresponding KSs is defined for classes andtheir attributes from the application ontology. In otherwords, a set of pairs{(oi, qj)|oi ∈ O, qj ∈ Q, ∃cn ∈CI : cn = (oi, qj)} from the AO is connected withcorresponding KSs. This set is stored in the knowledgemap and used for preparation of a user-oriented KSnetwork configuration by the configuration agent (seeSection 3.8).

3.6. Multi-agent architecture

Like some other KM systems, the system “KSNet”uses intelligent software agents to provide access todistributed heterogeneous KSs. Multi-agent systemsoffer an efficient way to understand, manage, and usethe distributed, large-scale, dynamic, open, and het-erogeneous computing and information systems[46].Multi-agent system architecture based on Founda-tion for Intelligent Physical Agents (FIPA) ReferenceModel [14] was chosen as a technological basis for thesystem since it provides standards for heterogeneousinteracting agents and agent-based systems, and spec-

68 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

Fig. 6. Multi-agent community of the system “KSNet”.

ifies ontologies and negotiation protocols. FIPA-basedtechnological kernel agents used in the system are:wrapper (interaction with KSs), facilitator (“yellowpages” directory service for the agents), mediator(task execution control), and user agent (interactionwith users). The following problem-oriented agentsspecific for KL and scenarios for their collaborationwere developed: translation agent (terms translationbetween different vocabularies), KF agent (KF op-eration performance), configuration agent (efficientuse of KSNet), ontology management agent (ontol-ogy operations performance), expert assistant agent(interaction with experts), and monitoring agent (KSs

Table 1Agents’ connectivity matrix (P: peer-to-peer interaction, M: mediating interaction)

Caller Callee

Wrapper Mediator Facilitator Useragent

Translationagent

EAagent

Configurationagent

KFagent

Monitoringagent

OMagent

Wrapper P P P PMediator P P P P P P PFacilitator PUser agent M/P PTranslation agent PExpert assistant

(EA) agentM/P P

Configuration agent M/P P P M/PKF agent P P PMonitoring agent M/POntology management

(OM) agentP P P

verifications). The multi-agent architecture is givenin Fig. 6 and is described in detail in[33,35]. Twotypes of agent interactions are used: (i) peer-to-peerinteraction assuming negotiation and (ii) mediatedinteraction assuming “master–slave” relation. The in-teractions between the agents are presented inTable 1.

In order to increase rapidity of the KF process inthe system “KSNet” the following supporting taskswere defined (Fig. 7): (i) the knowledge map creationutilizing alternative KSs ranking, (ii) KS network con-figuration based on the task of efficient KSs choice,and (iii) user request processing based on constraintnetwork processing. These tasks require development

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 69

System Tasks Problems Techniques

Ontology Engineering & Management

Knowledge Map Creation

KSNet Configuration

Configuration Management

Alternative KSs Ranking

Group Decision Making

Genetic Algorithm

Constraint Satisfaction

Operations on Ontologies

Object-Oriented Constraint Networks

Ontology Management

Agent

Monitoring Agent

Configuration Agent

User Request Processing

Knowledge Fusion Agent

Knowledge Generation &

Validation

Agent Type

Fig. 7. Main system tasks and techniques.

Table 2KM areas support by users and agents

Area of KM User Agent

Knowledge storage Administrator Monitoring agentSoftware engineer Ontology management agent

Knowledge sharing Ontology engineer Ontology management agentSoftware engineer Wrapper

Knowledge reuse Ontology engineer Configuration agentKnowledge engineer Monitoring agentSoftware engineer Wrapper

Ontology management Expert User agentOntology engineer Expert assistant agentKnowledge engineer Ontology management agent

MediatorTranslation agent

Knowledge revealing Ontology engineer User agentKnowledge engineer

Knowledge generation Software engineer KF agent

Knowledge entry Expert User agentKnowledge engineer Expert assistant agent

Mediator

Knowledge integration Knowledge engineer KF agentSoftware engineer Monitoring agent

Ontology management agent

Knowledge transportation Software engineer WrapperMediator

Knowledge search Experts Monitoring agentOntology engineer Ontology management agentKnowledge engineer

Knowledge indexing Administrator Monitoring agent

and application of appropriate mathematical mecha-nisms (models and methods) and their performanceis supported by problem-oriented system agents. InSection 3.8one of these tasks is presented in detail.

Table 2presents areas of KM the knowledge logis-tics deals with, and a list of the system agents andusers supporting these areas.

3.7. Knowledge fusion patterns

In the system “KSNet” the knowledge fusion takesplace during performance of a number of tasks. Car-ried out analysis allowed to select a list of the follow-ing generic KF patterns for these operations (Fig. 8):

• Selective fusion(AO and KSO creation). New KS iscreated which contains required parts of the initial

70 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

A a1

a2 a3

a4

B b1

b2 b3

b4

Initial Knowledge Sources

A a1

a2 a3

a4

B b1

b2 b3

b4

C a4

a3 b2

b1

Selective Fusion

A a1

a2 a3

a4

B b1

b2 b3

b4

C

Simple Fusion

A a1

a2 a3

a4

B b1

b2 b3

b4 b1

b2

Extension

a1

a2 a3

a4

b1

b2

b3

b4

C

Flat Fusion

a1

a2 a3

a4

B b1

b2 b3

b4

A

Absorption

Fig. 8. Knowledge fusion patterns.

KSs. Initial KSs preserve their internal structuresand autonomy.

• Simple fusion(OL creation and maintenance). NewKS is created which contains initial KSs. Initial KSspreserve their internal structures and lose (partiallyor completely) their autonomy.

• Extension(the knowledge map and internal KBmaintenance). One of initial KS is extended sothat it includes the required part of other initial KSwhich preserves its internal structure and autonomy.

• Absorption(a new KS connection to the system).One of initial KSs is extended so that it includesother initial KS which preserves its internal struc-ture and loses (partially or completely) its auton-omy.

• Flat fusion (KF for user request processing). NewKS is created which contains initial KSs. The initialKSs dissolve within new KS and do not preservetheir internal structures and autonomy.

Developed KF patterns are illustrated via the fol-lowing example. Two initial KSs (A and B) with somestructures of primary knowledge units are given. Thereis a tacit relationship between two primary knowl-edge units, namely a3 from A and b2 from B. It isnecessary to fuse two sources preserving the inter-nal knowledge structure and revealing the above tacitrelationship.

Use of the KF patterns accelerates the KF processdue to typification of fusion schemes.Table 3presents

KF patterns and system agents used for major KLoperations performance.

3.8. Utilizing genetic algorithms for knowledgesource network configuration

The goal of this task is a selection by the con-figuration agent of KSs which can be used for userrequest processing in the most efficient way accordingto the predefined criteria such as costs and/or time.The task of efficient KSs choice can be defined as aconfiguration of feasible (in accordance with a givenset of structural constraints) and efficient (in accor-dance with a given criteria) KS network and definitionof a set of rules prescribing when to use a certainKS.

The system “KSNet” includes:

• The AO containing some ontology (knowledge) ele-ments (OE) ({aj}): AO = (O, Q, D, C) = {aj}nj=1,wheren is the number of the OEs.

• KSs Si containing some OEs and described bypreliminary KSOs at a time instantt: A(Sit) =(O(Sit), Q(Sit), D(Sit), C(Sit)) = {slit }, l = 1, . . . ,

Lit; i = 1, . . . , m; t = 1, . . . , T , whereLit is thenumber of OEs of KSi, m the number of KSsin the system, andT the lifetime of the system“KSNet”.

• Knowledge map associating OEs of AO with KSsat a time instantt. Such association is denoted by

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 71

Table 3Knowledge fusion patterns and system agents used for major KL operations performance

Operation KF pattern Agent

Ontologies library creation Simple fusion User agentExtension Expert assistant agent

MediatorMonitoring agentOntology management agentFacilitator

Application ontology creation Selective fusion User agentExtension Expert assistant agent

MediatorMonitoring agentOntology management agentFacilitator

Request ontology creation Absorption User agentSelective fusion Expert assistant agentExtension Mediator

Monitoring agentOntology management agentFacilitator

Knowledge source introduction into the system andknowledge source ontology creation

Absorption User agent

Selective fusion Expert assistant agentExtension Mediator

Monitoring agentOntology management agentFacilitator

Knowledge map creation Extension User agentExpert assistant agentMediatorMonitoring agentFacilitator

New knowledge entry by experts Extension User agentExpert assistant agentMediatorMonitoring agentFacilitatorTranslation agentOntology management agent

User request processing Flat fusion User agentExtension Mediator

Monitoring agentFacilitatorTranslation agentOntology management agentWrapperConfiguration agentKF agent

KF operation—resolution of constraint networkdefined by partial AO for request processing

Flat fusion MediatorKF agent

Storing of the results of user request processing Extension MediatorMonitoring agentFacilitator

72 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

“→”, and a statement “OEaj is associated with KSSit” is denoted by(aj → Sit) : KM t = {(aj →Sit)}, aj ∈ A.KSO is an association of KS’ elementsto the AO’s elements:A(Sit) = {(aj → slit )}.

When user requestR is received by the system it isdecomposed into a set of subrequestsrk, to be associ-ated with AO’s OEs (i.e. translated into the system’sterms). This association is contained in the requestontology A(R). Generally, the RO contains elementsfor more than one request, however, here a simplifiedcase is considered when the RO describes only one re-quest, what does not influence upon a solution. Thenthe system “KSNet” has the request translated anddecomposed into subrequests associated with AO’sOEs. Such requests are denoted byR′ : R = {rk}, k =1, . . . , K, A(R) = {(rk → aj)}, rk ∈ R, aj ∈ A,R′ = {aj}, ∀aj∃(rk → aj) ∈ A(R), whereK is thenumber of subrequests. When the above operationsare completed a set of feasible decisions of the taskDecR can be written as: DecR = {decR} , decR ={(aj → Sit)}, aj ∈ A(R). Reliability Reli, costsCost or time Time required for request processingcan be used as criteria of the decision’s effective-ness: Reli= fReli(decR), Cost = fCost(decR) andTime = fTime(decR). A decision is considered effec-tive (denoted by deceff

R ) if the value of the goal func-tion is minimal with the above constraints being true:deceff

R ∈ DecR∀decR ∈ DecR, fCost(deceffR ) ≤ fCost

(decR).For this task a Genetic Algorithm (GA) was used

[6] as a probabilistic approach to pseudooptimal so-lutions search. Initially a random set of solutions isgenerated, then solutions are estimated, the set issorted according to the chosen criteria, and mutationmechanism is applied to the best solution to gener-ate new solutions. The newly generated solutions aresorted, and the new iteration is performed (Fig. 9).The process is stopped after a predefined number ofiterations.

For GA application the following notations areused. A feasible static decision decR is defined as the“chromosome” with the following structure: decR ={decRj,i}, where each decRj,i is a Boolean variable equalto 1 if KSi is used for obtaining OEk or to 0 other-wise. To simplify further discussion the OEs ofR′are renumbered in the following way:R′ = {aj}, j =1, . . . , n, n = |R′|. Hence, decR represents a binary

matrix:

To investigate an efficiency of GA a set of experi-ments with a basic GA for tasks of different dimen-sions have been performed, with KS’ parameters andknowledge maps being randomly generated. The re-ceived results indicate that the number of required cal-culations for obtaining a quasi-efficient decision evenusing the basic non-optimized GA is smaller than thatin the exhaustive search method.Fig. 10represents the

4 2 1 3 5

1 2 3 4 5

Generation of initial solution set (first generation)

Evaluation of decisions according to chosen criteria (fitting)

Sorting of decision set from best to worst

Mutation of the best solution for new solution set generation

Selection of the best solution

Fig. 9. Genetic algorithm implementation scheme.

Fig. 10. Efficiency improvement due to GA application.

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 73

Fig. 11. Prototype architecture.

ratio of calculations number for the exhaustive searchmethod to that for the GA, and this improvement growsnon-linearly along with the task dimension growth.

4. Prototype of the system “KSNet”

The main goal of the described below researchprototype architecture was to check possibility ofimplementation of proposed technologies for KSNet-approach and to test using of the KSNet-approach forcomplex dynamic systems—“product–process–busi-ness organization (business)” systems—of differentconfiguration types: (i) marketing/order configuration,(ii) product configuration, (iii) upgrade/add-on con-figuration, (iv) distributed process configuration, (v)business network unit configuration, and (vi) wholebusiness network configuration.

The client–server architecture of the research proto-type of the system “KSNet” is presented inFig. 11. Itwas chosen in accordance with the following reasons:(i) minimization of requirements to user comput-ers (web-based application allows user to have onlyHTML-compatible web-browser and access to the In-ternet), (ii) requirement of processing large amountsof information received from distributed KSs on thecentral (server) computer, (iii) a necessity to havean access to ILOG key server located in the localnetwork, and (iv) specifics of the agent communityimplementation.

The client interface is implemented as HTMLpages with JavaScripts presented to users via MSInternet Explorer. The server part is implemented us-ing PHP v.4.2.3, MS Access XP, MS Visual FoxProand MS Access ODBC drivers, MS Visual Studio 6.0(Visual C++, Visual FoxPro) and constraint satis-

74 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

faction/propagation technology ILOG (Configurator2.0, Solver 5.0, Concert Technology 1.0)[17]. Thegroup decision support system “MultiExpert”[32] isused for the knowledge map creation. The applicationbased on genetic algorithm is used for KS networkconfiguration. The application based on ILOG con-straint satisfaction technology is used for constraintnetwork processing.

Among developed and tested functions of the re-search prototype of the system “KSNet” the followingones can be mentioned:

Ontology management. The main criteria for im-plementation of OL were: (i) support of the chosenontology representation formalism, (ii) compatibilitywith other formats such as DAML+ OIL, and (iii)availability of a web-based interface enabling remotecollaborative work with ontologies. Some tools forontology creation/management (Ontolingua, OilEd,and Protégé) have been tested but none of them metall the above criteria. Thereby the client–server archi-tecture of the Web-DESO (Web-DEsign of StructuredObjects) has been developed to match the above cri-teria. In order to ensure that the designed prototype iscompatible with other standards a mechanism of im-port/export of ontologies described in DAML+ OILhas been developed and implemented.

Interaction between agentswas implemented usingthe system MAS DK[15] developed in SPIIRAS. Thissystem uses KQML for messaging between agents. It

Object-Oriented Constraint Networks

Internal Object Scheme

Classes Attributes Constraints Domains

ODBC

ISAPI / CGI

XML HTML / VRML

RDF

DAML+OIL

Data Management

User Interface

Ontology Management

Notation

Implementation

Representation

VRML Vi rtual Reality Marjup Language ISAPI I nternet Server Application Programming Interface CGI Com mon Gateway Interface ODBC Open DataBase Connection

DAML - The DARPA Agent Markup Language OIL T he Ontology Inference Layer RDF Resource Description Framework XML eXtensible Markup Language HTML Hyper Text Markup Language

Fig. 12. Standards of knowledge logistics information kernel.

provides mechanisms for scenarios error tracking us-ing return codes of agents’ functions, and allows rapiderror analysis using logs with differentiation of mes-sages in accordance with the level of importance. Allthe agent scenarios were designed using UML-basedconceptual projects.

KS network configurationimplements the geneticalgorithm. It is executed by configuration agent dur-ing user request processing (building of KS networkconfiguration). The function developed in VisualC++ and stored in a DLL library.

Knowledge fusion. For implementation of constraintnetworks utilizing for KF operations ILOG Configu-rator[17] is used. These functions are executed by KFagent during user request processing (knowledge inte-gration from different KSs). The functions developedin Visual C++ and stored in a DLL library.

Request recognition. For syntax user request recog-nition a mechanism of regular expressions was appliedby using a freely distributed library (a part of Ac-tivePerl language[1] developed by Activestate[2]) inVisual C++. The function is executed by translationagent during user request processing (recognition ofa request entered in an arbitrary form). The functionstored in a DLL library.

In accordance with up-to-date technologies andstandards the information kernel for KL was builtas shown inFig. 12. As it was described abovethe knowledge in the system is represented by an

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 75

aggregate of interrelated classes, their attributes,attributes’ domains, and relations between them. Anobject scheme for working with the knowledge and adatabase structure for its internal storage are designedbased on this notation. An access to the database isperformed via ODBC as a standard data access mech-anism under MS Windows. Remote access to thestored knowledge is performed via common HTTP

Fig. 13. User request input and output forms for car configuration and resource allocation examples.

Internet protocol. Knowledge is represented by eitherinteractive HTML+ VRML JavaScript enabled pagesfor users or a format based on DAML+ OIL forknowledge-based tools.

The interface forms for all “KSNet” system userswere developed for the software prototype. Two typesof interface forms were designed for knowledge cus-tomers: (i) request input in an arbitrary form and (ii)

76 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

problem-oriented structured request input calleduserrequest templates. When the system “KSNet” startsworking it does not have any request templates. Usersinput requests in an arbitrary form and the system ac-cumulates information about users’ requests and in-terests.

Fig. 13presents two examples of the developed userrequest templates: (i) resource allocation example: aproduction system for car assembly and (ii) prod-uct configuration example: a car configuration. Sincethe examples are implemented as web-based applica-tions they can also be considered as a prototype of“Multi-component product e-configuration tool”[5].

5. Discussion and future work

Comparison of the system “KSNet” with some otherexisting systems/projects oriented to knowledge inte-gration is presented inTable 4. They are:

• KRAFT (Knowledge Reuse and Fusion/Transformation)is a multi-agent system for integration of heteroge-neous information systems.

• InfoSleuth is a multi-agent system for retrieving andprocessing information in a network of heteroge-neous information sources.

Table 4Comparison of the system “KSNet” with existing knowledge/information integration systems

Characteristic KRAFT InfoSleuth KSNet

Languages and formats used KQML, P/FDM, CoLan, CIF Initially KQML, KIF; currentlyOKBC. Initially ODBC;currently JDBC; LISP, CLISP,LDL+ Java, C/C++, NetScape

KQML, KIF, DAML + OIL,MS Visual C++, ILOG, MSVisual FoxPro, HTML,JavaScript

Supported sources Any available informationsources for which appropriateprocessing mechanisms exist

Initially databases; currently anyavailable sources for whichappropriate processingmechanisms exist

Any available sources forwhich appropriate processingmechanisms exist

Multi-agent architecture FIPA-based with peer-to-peerinteraction

FIPA-based with mediatinginteraction

FIPA-based with mixedpeer-to-peer and mediatinginteraction

Relationships betweenontologies

Hierarchy Mapping of sources ontologiesto the system ontology

Mapping of sources ontologiesonto current application ontology

Peculiarities Processes data and constraints The network of interactingagents is developed.Mechanisms of messagesinterchange in multi-agentsystems are described

Utilizes object-orientedconstraint networks forknowledge representation

Case study Virtual enterprises Environmental date exchangenetwork (EDEN) project

E-business, virtual enterprises

Some tasks of the above projects are devoted to aresimilar to the “KSNet” system’ tasks (e.g. informationretrieval and processing). But they offer rather lim-ited techniques for searching effective solutions undergiven constraints and criteria (e.g. costs or time) andestimating effectiveness of obtained solution. Besides,they do not consider experts as KSs. However, thedirect knowledge input by domain experts enablesintroducing new knowledge not available from othersources. KF technology allows to generate newknowledge that does not exist in any KSs. New knowl-edge is stored in the system repository for its furtherreuse.

Some advantages of the proposed KSNet-approachare presented below:

• using OL facilitates an application domain descrip-tion;

• using AO terms and the top-level ontology notationsfacilitates the process of KF, increases results relia-bility, and minimizes possible loss of information;

• translation of ontologies from modern formats(RDF, DAML + OIL, etc.) into internal representa-tion and back enables ontologies interchange withother OLs;

• using the knowledge map facilitates and acceleratesthe KS network configuration;

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 77

• KSNet-approach is based on a synergistic use ofknowledge from multiple sources, and provides agood basis for a personalized service of users—knowledge customers. It attempts to apply the ideaof mass customization to the system “KSNet” serv-ing their users.

Knowledge Flow Reference Model[47] based onthe idea of knowledge grid can introduce noticeableimprovements into the KL technology. Namely, theimplementation of cooperation between team mem-bers on the level of knowledge can stabilize the team’slevel of knowledge by weakening the dependency ofits work on its members. The knowledge flow modelis oriented to propagation of knowledge within dis-tributed cooperative teams and KL deals with satis-faction of knowledge customers’ needs. Therefore, anintegration of the proposed here KSNet-approach withthe idea of knowledge flow network could increase thequality of service in knowledge grid environment.

The ongoing work includes:

• Development of models, agent architectures andprototypes for knowledge sharing by knowledgemaps, and for the management of distributed un-certain knowledge and examining the effectivenessof the proposed approach in more practical appli-cations.

• An implementation of virtual reality-based ontol-ogy engineering environment using VRML[45].This will increase its efficiency due to combinationof modeled images with the natural for human 3Dperception of the world.

• Investigation and development of the problem-oriented agents’ negotiation/cooperation modelsand algorithms for KS network configuration whichwere earlier developed and discussed in[36].

6. Conclusions

Knowledge logistics in information grid environ-ment is a new direction of knowledge management.The paper discusses techniques, supporting proce-dures/tasks used for implementation of knowledge lo-gistics systems based on the being developed KSNet-approach. The description of multi-agent architectureof the knowledge logistics system “KSNet” basedon this approach is given. The structure and majorfeatures of software prototype are presented. Carried

out experiments proved applicability of the developedtechniques to such areas as management, productconfiguration, and supply chain[37]. Consequently,application of this approach could be useful forsuch fields as e-business, configuration management,strategic planning, etc. Utilizing ontologies and com-patibility of the employed ontology notation withmodern standards (such as DAML+ OIL) allowsseamless integration of the developed approach intoexisting processes in the described areas. The com-ponents of the knowledge repository enable utilizingheterogeneous knowledge sources due to the applica-tion of top-level ontology, provide scalability due toexpandable/renewable internal knowledge base, andallow rapid knowledge search due to the applicationof the knowledge map and user profiles.

Acknowledgements

Some parts of the research were done under thepartner project ISTC #1993P funded by EOARD, theproject #2.44 of the research program “Mathemat-ical Modeling, Intelligent Systems and Non-linearMechanical Systems Control” of the Presidium ofthe Russian Academy of Sciences, the grant #02-01-00284 of the Russian Foundation for Basic Research,and the project of the research program “Fundamen-tal Basics of Information Technologies and ComputerSystems” of the Russian Academy of Sciences.

References

[1] ActivePerl, The industry-standard Perl distribution forLinux, Solaris, and Windows, ActiveState, 2002.http://www.activestate.com/Products/ActivePerl/.

[2] ActiveState Homepage, 2002.http://www.activestate.com/.[3] M.B. Adams, O.L. Deutsch, W.D. Hall, et al., Closed-loop

operation of large-scale enterprise: application of a decom-position approach, in: Proceedings of the DARPA ISOSymposium on Advances in Enterprise Control (AEC),San-Diego, USA, 2000, pp. 1–10.

[4] J.L. Aguirre, R. Brena, F.J. Cantu, Multiagent-based know-ledge networks, Expert Syst. Appl. 20 (2001) 65–75.

[5] H. Akkermans, OBELIX Project Proposal: The EU e-BusinessOntology Project, Vrije Universiteit, 2001.http://www.ontoweb.org/workshop/ucecOntoWeb/obelix.ppt.

[6] E. Bach, E. Glaser, A. Condon, C. Tanguay, DNA models andalgorithms for NP-complete problems, in: Proceedings of the11th Annual IEEE Conference on Computational Complexity(CCC’96), Philadelphia, PA, USA, 1996, pp. 290–300.

78 A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79

[7] J. Blythe, J. Kim, S. Ramachandran, Y. Gil, An integratedenvironment for knowledge acquisition, in: Proceedings ofthe International Conference on Intelligent User Interfaces,Santa Fe, New Mexico, January 2001, pp. 13–20.

[8] M. Ceruti, C. Anken, A. Lin, S. Rubin, Applications of high-performance knowledge-based technology, in: Proceedingsof the IEEE Systems Man and Cybernetics, vol. 3,Nashville, TN, USA, October 8–11, 2000, pp. 1965–1971.http://citeseer.nj.nec.com/515062.html.

[9] V.K. Chaudhri, J.D. Lowrance, M.E. Stickel, J.F. Thomere,R.J. Wadlinger, Ontology construction toolkit, SRI Project No.1633, Report, Technical Note Ontology, AI Center, January2000, p. 85.

[10] COGITO: E-Commerce with Guaiding Agents Basedon Personalised Interaction Tools, 2002.http://ipsi.fhg.de/∼cogito.

[11] DAML—The DARPA Agent Markup Language Homepage,2001.http://www.daml.org.

[12] DAML + OIL, March 2001. http://www.daml.org/2001/03/daml+oil-index.html.

[13] DARPA Advanced Logistics Project, 2002.http://www.darpa.mil/iso/alp.

[14] FIPA Documentation, Foundation for Intelligent PhysicalAgents (FIPA), Geneva, Switzerland, 2003.http://www.fipa.org.

[15] V. Gorodetski, O. Karsayev, I. Kotenko, A. Khabalov,Software development kit for multi-agent systems design andimplementation, in: B. Dunin-Keplicz, E. Nawarecki (Eds.),Proceedings of the Second International Workshop of Centraland Eastern Europe on Multi-agent Systems (CEEMAS’01),Krakow, Poland, September 26–29, 2001, pp. 99–108.

[16] C.V. Holsapple, M. Singh, The knowledge chain model:activities for competitiveness, Expert Syst. Appl. 20 (2001)77–98.

[17] ILOG Corporate Web-site, 2001.http://www.ilog.com.[18] Y. Jarrar, G. Schiuma, M. Zairi, Defining organisational

knowledge: a best practice perspective, in: Proceedings ofthe Sixth International Conference on Quality InnovationKnowledge, Kuala Lumpur, Malaysia, February 17–20, 2002,pp. 486–496.

[19] Hummingbird Web Portal, 2002.http://www.hummingbird.com/products/eip.

[20] K. Kurbel, I.A. Loutchko, Framework for multi-agentelectronic marketplaces: analysis and classification of existingsystems, in: Proceedings of the International NAISO Congresson Information Science Innovations (ISI’2001), Dubai, UAE,March 17–21, 2001.http://www.wiwi.euv-frankfurt-o.de/wi-www/de/research/project/fpsoftcompveroeff pdf/Anlage203.pdf.

[21] A. Maedche, S. Staab, Discovering conceptual relations fromtext, in: W. Horn (Ed.), Proceedings of the 14th EuropeanConference on Artificial Intelligence (ECAI 2000), IOS Press,Amsterdam, 2000. http://www.aifb.uni-karlsruhe.de/WBS/sst/Research/Publications/Publications.htm.

[22] E. Mena, V. Kashyap, A. Sheth, A. Illarramendi, OBSERVER:an approach for query processing in global informationsystems based on interoperation across pre-existingontologies, Distr. Parallel Databases 8 (2) (2000) 223–271.

[23] Microsoft SharePoint, Products and Technologies, 2002.http://www.microsoft.com/sharepoint.

[24] M.H. Nodine, A. Unruh, Facilitating open communicatingin agent systems: the InfoSleuth infrastructure, TechnicalReport MCC-INSL-056-97, Microelectronics and ComputerTechnology Corporation, Austin, TX, 1997.http://www.mmrg.ecs.soton.ac.uk/publications/archive/beltagy1998/belt-agy1998.ps.

[25] Ontoprise: Semantics for the WEB, 2001.http://www.ontoprise.de/com/startproducts.htm.

[26] Ontolingua, Stanford University, Knowledge SystemsLaboratory, 2001. http://www-ksl-svc.stanford.edu:5915/&service=frame-editor.

[27] A. Pease, V. Chaudhri, F. Lehmann, A. Farquhar, Practicalknowledge representation and the DARPA high performanceknowledge bases project, in: A. Cohn, F. Giunchiglia, B.Selman (Eds.), Proceedings of the Conference on KnowledgeRepresentation and Reasoning (KR-2000), San Francisco,CA, 2000, Morgan Kaufmann, Los Altos, CA.http://projects.teknowledge.com/HPKB/Publications/KR-195.ps.

[28] Protégé-2000 Project, Stanford Medical Informatics at theStanford University School of Medicine, USA, 2000.http://protege.stanford.edu/index.shtml.

[29] D.W. Rasmus, Knowledge management: more than AI but lesswithout it, in: PC AI, vol. 14, No. 2, Knowledge TechnologyInc., Phoenix, AZ, 2000, pp. 35–44.

[30] D. de Roure, N.R. Jennings, N. Shadbolt, The semantic grid:a future e-Science infrastructure, Int. J. Concurr. Comput.(2003) http://aspen.ucs.indiana.edu/CCPEwebresource/c590-gridderoure/finalsource/c590semgrid-journal-v5.doc.

[31] A. Smirnov, C. Chandra, Ontology-based knowledgemanagement for co-operative supply chain configuration,in: Proceedings of the 2000 AAAI Spring Symposium onBringing Knowledge to Business Processes, Stanford, CA,AAAI Press, 2000, pp. 85–92.

[32] A. Smirnov, M. Pashkin, I. Rakhmanova, Multi-agent WWW-based environment for distributed team decision makingsupport, in: Proceedings of Benefit Day on Pan-EuropeanCo-operation and Technology Transfer, Budapest, Hungary,1997, pp. 23–29.

[33] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Multi-agentarchitecture for knowledge fusion from distributed sources,Lecture Notes in Artificial Intelligence, No. 2296, Springer,Berlin, 2002, pp. 293–302.

[34] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova,Business knowledge logistics: an approach and technologyframework, in: Proceedings of the Sixth InternationalResearch Conference on Quality, Innovation & KnowledgeManagement (QIK’2002), Kuala Lumpur, Malaysia, February17–20, 2002, pp. 936–945.

[35] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova,Agent-based knowledge fusion in scalable informationenvironment: major principles and system framework, in:Proceedings of the First International ICSC Congresson Autonomous Intelligent Systems (ICAIS’2002), DeakinUniversity, Geelong, Australia, February 12–15, 2002.ISBN:3-906454-30-4 (electronic proceedings).

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 79

[36] A. Smirnov, M. Pashkin, N. Chilov, Agent architecture andnegotiation protocol for knowledge logistics in distributedintelligent enterprise, in: A. Dolgui, J. Sofdek, O. Zaikin(Eds.), Production System Design, Supply Chain Managementand Logistic, Proceedings of the Ninth International Multi-Conference on Advanced Computer Systems (ACS’2002),Part 2, Szczecin, Poland, 2002, pp. 379–386.

[37] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Knowledgesource network configuration in e-Business environment, in:Proceedings of the 15th IFAC World Congress (IFAC’2002),Barcelona, Spain, July 21–26, 2002.http://aldebaran.elo.utfsm.cl/conferences/15-IFAC/data/content/01278/1278.pdf.

[38] A.V. Smirnov, Knowledge fusion in the business informationenvironment: concept and major technologies, in: Proceedingsof the Seventh International Conference on ConcurrentEnterprising (ICE’2001), Bremen, Germany, June, 2001,pp. 273–276.

[39] Y. Sure, A Tool-supported methodology for ontology-basedknowledge management, in: E. Stubkjaer (Ed.), The Ontologyand Modeling of Real Estate Transactions in EuropeanJurisdictions, International Land Management Series,Ashgate, 2002. http://www.informatik.uni-bremen.de/∼heiner/COST/Sure.doc.

[40] G. Tecuci, M. Boicu, D. Marcu, et al., Developmentand deployment of a disciple agent for center of gravityanalysis, in: Proceedings of the 18th National Conference onArtificial Intelligence and the 14th Conference on InnovativeApplications of Artificial Intelligence, Edmonton, Alta.,Canada, July 27–August 1, 2002, pp. 853–860.

[41] The future of distributed middleware: grid computing and webservices, in: Proceedings of the SIMC’s General Meeting,November, 2002.http://www.simc-inc.org/archive0203/Grid/.

[42] M. Ushhold, M. Gruninger, Ontologies: principles, methods,and applications, Knowledge Eng. Rev. 1 (1996) 96–137.

[43] E.F. Vail, Knowledge Mapping: Getting Started with Know-ledge Management, in: Information Systems Management(1999) 16–23.

[44] P.R.S. Visser, D.M. Jones, M.D. Beer, T.J.M. Bench-Capon,B.M. Diaz, M.J.R. Shave, Resolving ontological heterogeneityin the KRAFT project, in: Proceedings of the InternationalConference on Database and Expert System Applications(DEXA-99), Florence, Italy, Springer, LNCS, 1999.http://www.csc.liv.ac.uk/∼kraft/publications/dexa-99.ps.gz.

[45] Web 3D Consortium, 2002.http://www.vrml.com/.[46] G. Weiss, Introduction, in: G. Weiss (Ed.), Multiagent

Systems: A Modern Approach to Distributed Artificial Intelli-gence, MIT Press, Cambridge, MA, London, 2000, p. 8.

[47] H. Zhuge, A knowledge flow model for peer-to-peer teamknowledge sharing and management, Expert Syst. Appl. 23(1) (2002).

[48] H. Zhuge, A knowledge grid model and platform for globalknowledge sharing, Expert Syst. Appl. 22 (4) (2002) 313–320.

[49] H. Zhuge, VEGA-KG: a way to the knowledge web, in:Proceedings of the 11th International World Wide WebConference, Honolulu, Hawaii, USA, May 7–11, 2002.http://www2002.org/CDROM/poster/-53.pdf.

[50] H. Zhuge, Clustering soft-devices in the semantic grid,Comput. Sci. Eng. 4 (6) (2002) 60–62.

Alexander V. Smirnov, Prof., receivedhis ME, his PhD and DSc degrees in St.Petersburg, Russia, in 1979, 1984, and1994, respectively. He is a Deputy Di-rector for Research and a Head of Com-puter Aided Integrated Systems Labora-tory at St. Petersburg Institute for Infor-matics and Automation of the RussianAcademy of Sciences (SPIIRAS). He isa full professor of St. Petersburg State

Polytechnical University and St. Petersburg State Electrical Engi-neering University. His current research interests belong to areasof corporate knowledge management, multi-agent systems, groupdecision support systems, virtual enterprises, and supply chainmanagement. He has published more than 150 research papers inreviewed journals and proceedings of international conferences,books, and manuals.

Mikhail P. Pashk received his MAMfrom St. Petersburg State University,Russia, in 1992. He is a researcher atComputer Aided Integrated Systems Lab-oratory of SPIIRAS. His current researchinterests include multi-agent systems, on-tology engineering and group decisionsupport systems. He has published morethan 40 research papers in proceedingsof international conferences, books andmanuals.

Nikolai Chilov received his ME in St. Pe-tersburg State Technical University, Rus-sia, in 1998. He is a researcher at theComputer Aided Integrated Systems Lab-oratory of SPIIRAS. His current researchinterests belong to areas of virtual enter-prise configuration, supply chain manage-ment, knowledge management, ontologyengineering and multi-agent systems. Heis an author/co-author of more than 30

research papers published in proceedings of international confer-ences and books.

Tatiana Levashova received her MEdegree at St. Petersburg State Elec-trical Engineering University in 1986.She is a leader programmer at Com-puter Aided Integrated Systems Labora-tory of SPIIRAS. Her current researchis devoted to knowledge-related problemssuch as knowledge representation, knowl-edge management, and ontology manage-ment. She has published more than 15

papers in reviewed journals and proceedings of international con-ferences.