Knowledge logistics in information grid environment - CiteSeerX

Future Generation Computer Systems 20 (2004) 61–79

Knowledge logistics in information grid environment Alexander Smirnov∗ , Mikhail Pashkin, Nikolai Chilov, Tatiana Levashova St. 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 and timely information for knowledge management systems is important. Knowledge logistics is a new direction of knowledge management addressing this issue. It provides a set of activities for knowledge search, acquisition and integration from distributed sources located in information grid environment. The paper proposes a developed Knowledge Source Network approach (KSNet-approach) to knowledge logistics and its multi-agent architecture, and describes a research prototype of the system “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 range of applications require operating in information grid environment. This leads to an expansion of tools dealing with knowledge storing in the Internet and based on intensive use of WWW-technologies and such standards as XML, RDF, DAML + OIL, etc. [11,12]. Thus, it is possible to speak about an evolution of the information environment, incorporating end-users and knowledge sources (KSs), from “regular” (with fixed interactions between its elements) to “intelligent” (with flexible configuration of knowledge network in which humans are involved). Grid is a modern technology for parallel distributed system development that enables sharing, selection, and aggregation of resources distributed across “multiple” administrative domains based on ∗ Corresponding author. Tel.: +7-812-328-8071; fax: +7-812-328-4450. E-mail address: [email protected] (A. Smirnov).

their availability, capability, performance, cost, and users’ quality-of-service requirements [41]. An information grid environment consists of end-users (knowledge customers) and loosely coupled information/knowledge sources (experts, knowledge bases, repositories, documents, etc.). Growing importance of intelligent support of the knowledge customers causes a need for acquisition, integration, and transfer of the right knowledge from distributed sources located in information grid environment. This knowledge has to be delivered in the right context to the right person, in the right time for the right purpose. These activities called Knowledge Logistics (KL) are required for global awareness, dynamic planning and global information exchange in information grid environment. At present, there are number of projects based on the grid technology. KL tightly correlates with the idea of semantic grid [30,50]. It is a new direction in the knowledge management (KM) and enables evolving of information grid into knowledge grid (a set of well-organized knowledge together with a set of KM operations [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 based on the synergistic use of knowledge from multiple distributed sources. It can be considered as a basis for KL activities. The paper describes an approach to KL through KF called “Knowledge Source Network” (KSNet-approach) that allows to complement insufficient knowledge and obtain new knowledge [38]. The architecture developed for the KL system (called system “KSNet”) is based on this approach and utilizes such technologies as ontology management, intelligent agents, constraint satisfaction, soft computing, and other [34]. The paper has the following structure: (i) it shortly describes the state-of-the-art of the KM areas, (ii) presents major KL technologies (KF operations and an ontology-driven methodology), a knowledge repository structure and a multi-agent architecture of the system “KSNet”, and (iii) describes being developed research prototype of the system.

2. Knowledge management: state-of-the-art KM is defined as a complex set of relations between people, processes, and technology bound together with the cultural norms, like mentoring and knowledge sharing, which constitute an organization’s social capital [29]. KM includes the following major tasks: knowledge discovery (knowledge entry, capture of tacit knowledge, KF, etc.), knowledge representation (KB development, knowledge sharing and reuse, knowledge exchange, etc.), knowledge mapping (identifying KSs, indexing knowledge, making knowledge accessible) [8,18,43]. There are number of different approaches proposed and tools developed for these tasks solving based on the algorithms of data search and retrieval in large databases, technologies of data storage and representation, etc. Among them the following ones can be pointed out: Microsoft SharePoint Portal [23], SearchServer/KnowledgeServer [19], Text-To-Onto [21], etc. (knowledge searching and retrieving from different types of documents); Disciple-RKF [40], EXPECT [7], COGITO [10], OntoKick [39], etc. (knowledge acquisition from experts and tacit knowledge revealing); OntoEdit [25], Protégé [28], Ontolingua [26], etc. (ontologies engineering); HPKB [27], etc. (KBs organization and development); KRAFT [44], InfoSleuth [24], RICA [4],

OBSERVER [22] etc. (knowledge and information integration). All these approaches do not provide a whole KL problem solution but solve particular tasks. Possible applications of KL belong to the following areas: • Large-scale dynamic systems (enterprises) with distributed operations in an uncertain and rapidly changing environment, where the information collection, assimilation, integration, interpretation, and dissemination are needed [3]. • Focused logistics operations and/or web-enhanced logistics operations addressing sustainment, transportation, and end-to-end rapid supply to the final destination. In this area the distributed information management and real-time information/knowledge fusion to support continuous information and knowledge integration and exchange between all participants of the operations are needed [13].Markets via partnerships with different organizations, where the dynamic identification and analysis of information sources and providing for interoperability between market participants (players) in a semantic manner are needed [20]. For all the above areas management systems can be described as an organizational combination of people, technologies, procedures and information/knowledge. KL is based on individual user requirements, available KSs, and content analysis in the information grid environment. Hence, systems operating in this area must react dynamically to unforeseen changes and unexpected user needs, keep up-to-date resource value assessment data, support rapid execution of complex operations, and deliver personalized results to the users/knowledge customers. Here proposed approach to KL is based on the KF technology and therefore assumes integration of knowledge from different sources (probably, heterogeneous) into a combined resource in order to complement insufficient knowledge and obtain new knowledge.

3. KSNet-approach: major technologies 3.1. Knowledge Source Network A network of loosely coupled sources located in the information grid environment was called to as

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 t = t1 a1t

t = t2 a1t

a3t a2t

s1

s2

s3

s5

s6

s7

63

a3t a2t

s4

s8

S1

s9

s10

S2

s11

s13

s12

S3

s14

s15

S4

customer-oriented knowledge model versions at time instant t ajt S1, sl

customer-oriented knowledge models elements at time instant t S4

knowledge sources models knowledge sources models elements

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

a “Knowledge Source Network” (KS network). The term “KS network” originates from the concept of virtual organization based on the synergistic use of knowledge from multiple sources. Fig. 1 explains roughly basic concepts of the KS networks and their multi-level configuration. The upper level represents a customer-oriented knowledge model based on a fusion of knowledge acquired from KS network units (KSs) that constitute the lower level and contain their own knowledge models. 3.2. Knowledge fusion In [16] the most complete sequence of main operations for KF referred to as knowledge chain was proposed. It was used as a basis for development of KF process structure consisting of the following operations: (i) capturing knowledge from KSs and its translation into a form suitable for a supplementary use, (ii) acquisition of knowledge from external sources, (iii) selection of knowledge from internal sources (local KBs), (iv) knowledge generation: producing knowledge by discovering or deriving from existing knowledge, (v) internalization: changing system knowledge by saving acquired, selected, and generated knowledge, (vi) externalization: embedding

knowledge into system’s output for release it into the environment, (vii) KF management: planning, coordination, collaboration, and control of operations constituting the KF process. To increase the KF process rapidity it is necessary not only to find required sources but also to identify their usefulness for solving a particular problem. For this purpose it is reasonable to: (i) utilize techniques of knowledge/ontology reuse, (ii) perform indexation of stored knowledge, (iii) increase intelligibility of knowledge representation for the users, and (iv) apply user profiling. 3.3. Knowledge logistics system repository structure One of the main components of any KM system is a repository. Repositories have different structures, architectures and implementations depending on a purpose of a KM area the repository belongs to. In the developed KSNet-approach repository (Fig. 2) is used for storage of information about different information grid elements (KSs characteristics and knowledge model, user interest models, domain descriptions, etc.) and relations between them. The following three components in the repository structure were defined: (i) a semantic component is used for knowledge rep-

64


Semantic description component

Ontology Library

Knowledge map

User profile

Service component Physical component

Internal knowledge base

Repository

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

resentation in a common notation and terms; (ii) a service component is used for knowledge indexing and search; (iii) a physical component is used for knowledge storage and reuse. • The semantic component contains ontologies used to describe the domain terms and correspondence between terms of different ontologies. An ontology is a formal description of entities and their properties, relationships, constraints, and behaviors [42]. It provides a common terminology that captures key distinctions and is generic across many domains, facilitating translation of concepts among these domains. • The service component contains the following elements: ◦ A knowledge map including information about locations of KS network units and information about alternative sources containing similar information and KSs characteristics. Monitoring tools perform permanent checking of KSs availability and perform appropriate changes in the knowledge map. The knowledge map is meant to facilitate and speed up the process of the KSs choice. ◦ A user profile including an organized storage of information about a user, his (her) requests history, etc. This element is used for a number of purposes (faster search due to analyzing and utilizing request history and user preferences, Just-before-Time request processing, etc.).

• The physical component contains internal KB used for storage and verification of knowledge: (i) entered by experts, (ii) learnt from users (knowledge consumers), (iii) obtained as a result of the KF process, and (iv) acquired from KSs which are not free, not easily accessible, etc. 3.4. Knowledge representation formalism A formalism of object-oriented constraint networks has been chosen for the ontology representation [31]. An abstract KS network model is based on this formalism. This solution was mainly motivated by such factors as support of declarative representation, efficiency of dynamic constraint satisfaction, and problem modeling capability, maintainability, reusability, and extensibility of the object-oriented technology. According to the chosen formalism an ontology (A) is defined as (Fig. 3): A = (O, Q, D, C): O is a set of object classes (“classes”). Each of the entities in a class is considered as an instance of the class. This set consists of two subsets O = OI ∪ OII : OI = {o : ∃instance(o)} is a set of non-primitive classes, i.e. classes possessing a list of inherited attributes that specify necessary and sufficient conditions for definition of an instance as the class member, and OII = {o : ¬∃instance(o)} is a set of primitive classes, i.e. classes having only a set of attributes that specify the necessary condition only. Q is a set of class


Superclasses

Class C Class A

Class B

functional constraint Domain K domain to attribute accessory constraint Domain L

Domains

65

is_a relation (constraint)

compatibility constraint

Attribute F

Attribute G

Attributes attribute to class accessory constraint

Subclasses

Class AA

Class AB

uses associative relation (constraint)

has_part relation (constraint)

Class CA

Class BA

Fig. 3. Object-oriented constraint network.

attributes (“attributes”). D is a set of attribute domains (“domains”). C is a set of constraints. For chosen notation the following six types of constraints have been defined: C = CI ∪ CII ∪ CIII ∪ CIV ∪ CV ∪ C.VI • • • •

• •

CI

{cI },

cI

= = (o, q), o ∈ O, q ∈ Q: belonging of 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 structural constraints); CIV = {cIV }, cIV = o , o , type, o’ ∈ O, o ∈ O, o = o”: hierarchical relationships (hierarchical structural constraints) “is a” defining class taxonomy (type = 0), and “has part”/“part of” defining class hierarchy (type = 1); CV = {cV }, cV = ({o}), |{o}| ≥ 2, o ∈ O: associative relationships (“one-level” structural constraints); CVI = {cVI }, cVI = f({o}, {q}) → True ∨ False, |{o}| ≥ 0, |{q}| ≥ 0, o ∈ O, q ∈ Q: functional constraints referring to the names of classes and attributes.

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

3.5. Ontology-driven methodology for knowledge logistics The system “KSNet” oriented to intelligent support of its users (decision makers) applies ontologies for user request processing. The following ontology types for the system were defined: (i) top-level ontology describing notation for ontology representation in the system; (ii) application ontology (AO) describing an application domain in terms of domain and tasks & methods ontologies; (iii) preliminary KS ontology describing KS in KS’s terms and the top-level ontology notation; (iv) KS ontology (KSO) containing correspondence between terms of KS and AO; (v) preliminary request ontology describing user request in user’s terms (which are used by the user for requests input) and the top-level ontology notation, (vi) request ontology (RO) containing correspondence between terms of preliminary request ontology and AO; (vii) domain ontology representing static knowledge about a particular domain in terms of the domain; and (viii) tasks & methods ontology describing problem-solving knowledge in terms of a domain or high-level terms that are general for several domains. The ontologies are stored in a common ontology library (OL) that allows sharing and reusing them. The OL’s ontologies share a common notation provided by the top-level ontology. Domain ontologies

66

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

Domain Ontology

Legend:

m:1

−

ontology

−

"many-to-one" relationship

−

associative relationship

m:m m:m

m:1

1:m

Request Ontology 1:m

Application Ontology

Knowledge Source Ontology

1:m

m:1 1:1

1:1

Request

Preliminary Knowledge Source Ontology

Preliminary Request Ontology

Knowledge Source

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

and tasks & methods ontologies are formed as a new knowledge become available. The new knowledge here is knowledge provided by experts, retrieved from KSs, or obtained as results of user request processing. Both new ontologies can be created (if there is no ontology relating to domain/task/method of the new knowledge) and existing ontologies can be expanded (otherwise). Relationships between the ontologies of OL are presented in Fig. 4. Relationships between a domain ontology and a tasks & methods ontology are established if knowledge of the domain are used by a task or a method described by the tasks & methods ontology. According to the chosen formalism the tasks/methods of the tasks & methods ontology are described by ontology classes, output and input parameters of the tasks/methods are described by attributes of these classes. If an attribute value from a domain ontology can be considered as a parameter of a task/method then an associative relationship is established between a domain ontology class holding the attribute and a class of the tasks & methods ontology representing the task/method and vice versa.

Parts of domain ontologies and tasks & methods ontologies make up an AO. AO is a conceptual model describing a real-world application domain. It depends on a particular domain and problem. In case when a request has not been processed before parts of domain and tasks & methods ontologies relevant to the request are integrated into a new AO. Between classes of ontologies parts of which were included into AO and appropriate classes of AO relationships are established. Otherwise, an existing AO is reused. RO is formed by alignment of preliminary RO and AO that is to be used for the request processing. Alignment is establishing links between two ontologies via determination of a correspondence between their elements. Thus, each preliminary RO is related to one AO and one RO (Fig. 4). AO reuse corresponds to the one-to-many relationship between AO and preliminary RO in the figure. The associative relationship between RO and tasks & methods ontology illustrates the case when methods resolving semantic conflicts (e.g., “class–attribute–attribute value” mismatch, conflicts in classes subordination [9], etc.) between AO and preliminary RO are attached.


The request processing assumes searching for KSs. The one-to-many relationship between AO and KSO is explained by the fact that several KSs can be found for a request. The same way as every request has its preliminary RO, every KS is described by its preliminary KSO. The relationship one-to-many in Fig. 4 between preliminary KSO and KSO refers to by the fact that information from KSs relevant to the particular request and correspondingly to the certain AO is used. Preliminary KSO, can, contain extra knowledge with regard to such AO and request. Relevant to AO information is described by a part of preliminary KSO represented as a self-contained knowledge source ontology. The associative relationship between tasks & methods ontology and KSO has the same meaning as in the case with RO. A conceptual scheme of the user-oriented ontologydriven KL methodology is presented in Fig. 5. The system works in terms of a common vocabulary. Each user/user group works in terms of associated expandable RO and thereby with a part of AO pertinent to the user/user group. User profiles are used during interactions to provide for an efficient personalized service. Every user request consists of two parts: (i) structural constituent (containing the request terms and relations between them), and (ii) parametric constituent (containing additional user-defined constraints). For the request processing, an auxiliary KS network configuration is built defining when and what KSs are to be used for the request processing in the most efficient

User Profile

way. For this purpose the knowledge map including information about locations of KSs is used. Translation between the system’s and KS’ notations & terms is performed using KSOs. During KSO creation (when a new KS is attached to the system) and modification (when an appropriate KS is changed), a correspondence between KS terms and the AO terms is identified. As a result of this process a set of corresponding KSs is defined for classes and their attributes from the application ontology. In other words, a set of pairs {(oi , qj )|oi ∈ O, qj ∈ Q, ∃cn ∈ CI : cn = (oi , qj )} from the AO is connected with corresponding KSs. This set is stored in the knowledge map and used for preparation of a user-oriented KS network configuration by the configuration agent (see Section 3.8). 3.6. Multi-agent architecture Like some other KM systems, the system “KSNet” uses intelligent software agents to provide access to distributed heterogeneous KSs. Multi-agent systems offer an efficient way to understand, manage, and use the distributed, large-scale, dynamic, open, and heterogeneous computing and information systems [46]. Multi-agent system architecture based on Foundation for Intelligent Physical Agents (FIPA) Reference Model [14] was chosen as a technological basis for the system since it provides standards for heterogeneous interacting agents and agent-based systems, and spec-

User L ogistics Manager

Knowledge Source Ontology

Parametric Constituent User Request Structural Constituent

Application Ontology Constituent

Request Ontology User Constituent

Answer

Application Ontology Constituent

User Request Processing

Knowledge Map

Correspondence User

Operational Manager

67

Correspondence

Knowledge Source

Ontologies Application Library Ontology

Instances User E ngineer Request Ontology

Request Ontology User Profile

User Profile Domain Ontology

Knowledge Source Constituent

Passive Sources Databases, Knowledge Bases, Documents, etc.

Tasks & Methods Ontology

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

Active Sources Experts, Knowledgebased tools, etc.

68


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

ifies ontologies and negotiation protocols. FIPA-based technological kernel agents used in the system are: wrapper (interaction with KSs), facilitator (“yellow pages” directory service for the agents), mediator (task execution control), and user agent (interaction with users). The following problem-oriented agents specific for KL and scenarios for their collaboration were developed: translation agent (terms translation between different vocabularies), KF agent (KF operation performance), configuration agent (efficient use of KSNet), ontology management agent (ontology operations performance), expert assistant agent (interaction with experts), and monitoring agent (KSs

verifications). The multi-agent architecture is given in Fig. 6 and is described in detail in [33,35]. Two types of agent interactions are used: (i) peer-to-peer interaction assuming negotiation and (ii) mediated interaction assuming “master–slave” relation. The interactions between the agents are presented in Table 1. In order to increase rapidity of the KF process in the system “KSNet” the following supporting tasks were defined (Fig. 7): (i) the knowledge map creation utilizing alternative KSs ranking, (ii) KS network configuration based on the task of efficient KSs choice, and (iii) user request processing based on constraint network processing. These tasks require development

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

Callee Wrapper

Wrapper Mediator Facilitator User agent Translation agent Expert assistant (EA) agent Configuration agent KF agent Monitoring agent Ontology management (OM) agent

Mediator

Facilitator

P

P

P P M/P

P

M/P

P

P P M/P

P P

User agent

Translation EA agent agent

Configuration agent

KF agent

Monitoring OM agent agent

P

P P

P

P

P

P P

P

M/P

P

M/P P P

P

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

System Tasks

Problems

Knowledge Fusion Agent

User Request Processing

Knowledge Generation & Validation

Configuration Agent

KSNet Configuration

Techniques

Constraint Satisfaction

Configuration Management Genetic Algorithm

Monitoring Agent

Knowledge Map Creation

Alternative KSs Ranking

Group Decision Making

Ontology Management Agent

Ontology Engineering & Management

Operations on Ontologies

Object-Oriented Constraint Networks

Fig. 7. Main system tasks and techniques.

69

and application of appropriate mathematical mechanisms (models and methods) and their performance is supported by problem-oriented system agents. In Section 3.8 one of these tasks is presented in detail. Table 2 presents areas of KM the knowledge logistics deals with, and a list of the system agents and users supporting these areas. 3.7. Knowledge fusion patterns In the system “KSNet” the knowledge fusion takes place during performance of a number of tasks. Carried out analysis allowed to select a list of the following generic KF patterns for these operations (Fig. 8): • Selective fusion (AO and KSO creation). New KS is created which contains required parts of the initial

Table 2 KM areas support by users and agents Area of KM

User

Agent

Knowledge storage

Administrator Software engineer

Monitoring agent Ontology management agent

Knowledge sharing

Ontology engineer Software engineer

Ontology management agent Wrapper

Knowledge reuse

Ontology engineer Knowledge engineer Software engineer

Configuration agent Monitoring agent Wrapper

Ontology management

Expert Ontology engineer Knowledge engineer

User agent Expert assistant agent Ontology management agent Mediator Translation agent

Knowledge revealing

Ontology engineer Knowledge engineer

User agent

Knowledge generation

Software engineer

KF agent

Knowledge entry

Expert Knowledge engineer

User agent Expert assistant agent Mediator

Knowledge integration

Knowledge engineer Software engineer

KF agent Monitoring agent Ontology management agent

Knowledge transportation

Software engineer

Wrapper Mediator

Knowledge search

Experts Ontology engineer Knowledge engineer

Monitoring agent Ontology management agent

Knowledge indexing

Administrator

Monitoring agent

70

A. Smirnov et al. / Future Generation Computer Systems 20 (2004) 61–79 A a1

b1 B a4

a2

a3

b4

b2

b3

Initial Knowledge Sources a4 C a3 A a1 a2

C b1

A a1 a2

b2 b1 B

a4 a3

b2

A a1

b1 B a4 a3

b2

a2

b4 b3

b1

a4 a3

b2

b1 B b2

b4 b3

b4 b3

Extension

Simple Fusion Selective Fusion

C

A a1 a2

b3

b1 B a4 a3

b2

b4 b3

Absorption

a1

b1 b2

a2

a4

b4

a3

Flat Fusion

Fig. 8. Knowledge fusion patterns.

•

•

•

•

KSs. Initial KSs preserve their internal structures and autonomy. Simple fusion (OL creation and maintenance). New KS is created which contains initial KSs. Initial KSs preserve their internal structures and lose (partially or completely) their autonomy. Extension (the knowledge map and internal KB maintenance). One of initial KS is extended so that it includes the required part of other initial KS which preserves its internal structure and autonomy. Absorption (a new KS connection to the system). One of initial KSs is extended so that it includes other initial KS which preserves its internal structure and loses (partially or completely) its autonomy. Flat fusion (KF for user request processing). New KS is created which contains initial KSs. The initial KSs dissolve within new KS and do not preserve their internal structures and autonomy.

Developed KF patterns are illustrated via the following example. Two initial KSs (A and B) with some structures of primary knowledge units are given. There is a tacit relationship between two primary knowledge units, namely a3 from A and b2 from B. It is necessary to fuse two sources preserving the internal knowledge structure and revealing the above tacit relationship. Use of the KF patterns accelerates the KF process due to typification of fusion schemes. Table 3 presents

KF patterns and system agents used for major KL operations performance. 3.8. Utilizing genetic algorithms for knowledge source network configuration The goal of this task is a selection by the configuration agent of KSs which can be used for user request processing in the most efficient way according to the predefined criteria such as costs and/or time. The task of efficient KSs choice can be defined as a configuration of feasible (in accordance with a given set of structural constraints) and efficient (in accordance with a given criteria) KS network and definition of a set of rules prescribing when to use a certain KS. The system “KSNet” includes: • The AO containing some ontology (knowledge) elements (OE) ({aj }): AO = (O, Q, D, C) = {aj }nj=1 , where n is the number of the OEs. • KSs Si containing some OEs and described by preliminary KSOs at a time instant t: A(Sit ) = (O(Sit ), Q(Sit ), D(Sit ), C(Sit )) = {slit }, l = 1, . . . , Lit ; i = 1, . . . , m; t = 1, . . . , T , where Lit is the number of OEs of KSi , m the number of KSs in the system, and T the lifetime of the system “KSNet”. • Knowledge map associating OEs of AO with KSs at a time instant t. Such association is denoted by


71

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

KF pattern

Agent

Ontologies library creation

Simple fusion Extension

User agent Expert assistant agent Mediator Monitoring agent Ontology management agent Facilitator

Application ontology creation

Selective fusion Extension


Request ontology creation

Absorption Selective fusion Extension


Knowledge source introduction into the system and knowledge source ontology creation

Absorption

User agent

Selective fusion Extension

Expert assistant agent Mediator Monitoring agent Ontology management agent Facilitator

Knowledge map creation

Extension

User agent Expert assistant agent Mediator Monitoring agent Facilitator

New knowledge entry by experts

Extension

User agent Expert assistant agent Mediator Monitoring agent Facilitator Translation agent Ontology management agent

User request processing

Flat fusion Extension

User agent Mediator Monitoring agent Facilitator Translation agent Ontology management agent Wrapper Configuration agent KF agent

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

Flat fusion

Mediator KF agent

Storing of the results of user request processing

Extension

Mediator Monitoring agent Facilitator

72


“→”, and a statement “OE aj is associated with KS Sit ” is denoted by (aj → Sit ) : KMt = {(aj → Sit )}, aj ∈ A.KSO is an association of KS’ elements to the AO’s elements: A(Sit ) = {(aj → slit )}. When user request R is received by the system it is decomposed into a set of subrequests rk , to be associated with AO’s OEs (i.e. translated into the system’s terms). This association is contained in the request ontology A(R). Generally, the RO contains elements for more than one request, however, here a simplified case is considered when the RO describes only one request, what does not influence upon a solution. Then the system “KSNet” has the request translated and decomposed into subrequests associated with AO’s OEs. Such requests are denoted by R : R = {rk }, k = 1, . . . , K, A(R) = {(rk → aj )}, rk ∈ R, aj ∈ A, R = {aj }, ∀aj ∃(rk → aj ) ∈ A(R), where K is the number of subrequests. When the above operations are completed a set of feasible decisions of the task DecR can be written as: DecR = {decR } , decR = {(aj → Sit )}, aj ∈ A(R). Reliability Reli, costs Cost or time Time required for request processing can be used as criteria of the decision’s effectiveness: Reli = fReli (decR ), Cost = fCost (decR ) and Time = fTime (decR ). A decision is considered effective (denoted by deceff R ) if the value of the goal function is minimal with the above constraints being true: eff deceff R ∈ DecR ∀decR ∈ DecR , fCost (decR ) ≤ fCost (decR ). For this task a Genetic Algorithm (GA) was used [6] as a probabilistic approach to pseudooptimal solutions search. Initially a random set of solutions is generated, then solutions are estimated, the set is sorted according to the chosen criteria, and mutation mechanism is applied to the best solution to generate new solutions. The newly generated solutions are sorted, and the new iteration is performed (Fig. 9). The process is stopped after a predefined number of iterations. For GA application the following notations are used. A feasible static decision decR is defined as the “chromosome” with the following structure: decR = R {decR j,i }, where each decj,i is a Boolean variable equal to 1 if KSi is used for obtaining OEk or to 0 otherwise. To simplify further discussion the OEs of R 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 experiments with a basic GA for tasks of different dimensions have been performed, with KS’ parameters and knowledge maps being randomly generated. The received results indicate that the number of required calculations for obtaining a quasi-efficient decision even using the basic non-optimized GA is smaller than that in the exhaustive search method. Fig. 10 represents the Generation of initial solution set (first generation)

3 2 4 1 5

Evaluation of decisions according to chosen criteria (fitting)

1 2 3 4 5

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.


73

Fig. 11. Prototype architecture.

ratio of calculations number for the exhaustive search method to that for the GA, and this improvement grows non-linearly along with the task dimension growth.

4. Prototype of the system “KSNet” The main goal of the described below research prototype architecture was to check possibility of implementation of proposed technologies for KSNetapproach and to test using of the KSNet-approach for complex dynamic systems—“product–process–business organization (business)” systems—of different configuration types: (i) marketing/order configuration, (ii) product configuration, (iii) upgrade/add-on configuration, (iv) distributed process configuration, (v) business network unit configuration, and (vi) whole business network configuration.

The client–server architecture of the research prototype of the system “KSNet” is presented in Fig. 11. It was chosen in accordance with the following reasons: (i) minimization of requirements to user computers (web-based application allows user to have only HTML-compatible web-browser and access to the Internet), (ii) requirement of processing large amounts of information received from distributed KSs on the central (server) computer, (iii) a necessity to have an access to ILOG key server located in the local network, and (iv) specifics of the agent community implementation. The client interface is implemented as HTML pages with JavaScripts presented to users via MS Internet Explorer. The server part is implemented using PHP v.4.2.3, MS Access XP, MS Visual FoxPro and MS Access ODBC drivers, MS Visual Studio 6.0 (Visual C++, Visual FoxPro) and constraint satis-

74


faction/propagation technology ILOG (Configurator 2.0, Solver 5.0, Concert Technology 1.0) [17]. The group decision support system “MultiExpert” [32] is used for the knowledge map creation. The application based on genetic algorithm is used for KS network configuration. The application based on ILOG constraint satisfaction technology is used for constraint network processing. Among developed and tested functions of the research prototype of the system “KSNet” the following ones can be mentioned: Ontology management. The main criteria for implementation of OL were: (i) support of the chosen ontology representation formalism, (ii) compatibility with other formats such as DAML + OIL, and (iii) availability of a web-based interface enabling remote collaborative work with ontologies. Some tools for ontology creation/management (Ontolingua, OilEd, and Protégé) have been tested but none of them met all the above criteria. Thereby the client–server architecture of the Web-DESO (Web-DEsign of Structured Objects) has been developed to match the above criteria. In order to ensure that the designed prototype is compatible with other standards a mechanism of import/export of ontologies described in DAML + OIL has been developed and implemented. Interaction between agents was implemented using the system MAS DK [15] developed in SPIIRAS. This system uses KQML for messaging between agents. It

provides mechanisms for scenarios error tracking using return codes of agents’ functions, and allows rapid error analysis using logs with differentiation of messages in accordance with the level of importance. All the agent scenarios were designed using UML-based conceptual projects. KS network configuration implements the genetic algorithm. It is executed by configuration agent during user request processing (building of KS network configuration). The function developed in Visual C++ and stored in a DLL library. Knowledge fusion. For implementation of constraint networks utilizing for KF operations ILOG Configurator [17] is used. These functions are executed by KF agent during user request processing (knowledge integration from different KSs). The functions developed in Visual C++ and stored in a DLL library. Request recognition. For syntax user request recognition a mechanism of regular expressions was applied by using a freely distributed library (a part of ActivePerl language [1] developed by Activestate [2]) in Visual C++. The function is executed by translation agent during user request processing (recognition of a request entered in an arbitrary form). The function stored in a DLL library. In accordance with up-to-date technologies and standards the information kernel for KL was built as shown in Fig. 12. As it was described above the knowledge in the system is represented by an Ontology Management

User Interface

DAML+OIL RDF

Data Management

HTML / VRML

Representation

XML

ISAPI / CGI ODBC

Implementation

Internal Object Scheme Classes

Attributes

Constraints

Domains Notation

Object-Oriented Constraint Networks 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

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

Fig. 12. Standards of knowledge logistics information kernel.


aggregate of interrelated classes, their attributes, attributes’ domains, and relations between them. An object scheme for working with the knowledge and a database structure for its internal storage are designed based on this notation. An access to the database is performed via ODBC as a standard data access mechanism under MS Windows. Remote access to the stored knowledge is performed via common HTTP

75

Internet protocol. Knowledge is represented by either interactive HTML + VRML JavaScript enabled pages for users or a format based on DAML + OIL for knowledge-based tools. The interface forms for all “KSNet” system users were developed for the software prototype. Two types of interface forms were designed for knowledge customers: (i) request input in an arbitrary form and (ii)

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

76


problem-oriented structured request input called user request templates. When the system “KSNet” starts working it does not have any request templates. Users input requests in an arbitrary form and the system accumulates information about users’ requests and interests. Fig. 13 presents two examples of the developed user request templates: (i) resource allocation example: a production system for car assembly and (ii) product configuration example: a car configuration. Since the examples are implemented as web-based applications they can also be considered as a prototype of “Multi-component product e-configuration tool” [5].

Some tasks of the above projects are devoted to are similar to the “KSNet” system’ tasks (e.g. information retrieval and processing). But they offer rather limited techniques for searching effective solutions under given constraints and criteria (e.g. costs or time) and estimating effectiveness of obtained solution. Besides, they do not consider experts as KSs. However, the direct knowledge input by domain experts enables introducing new knowledge not available from other sources. KF technology allows to generate new knowledge that does not exist in any KSs. New knowledge is stored in the system repository for its further reuse. Some advantages of the proposed KSNet-approach are presented below:

5. Discussion and future work

• using OL facilitates an application domain description; Comparison of the system “KSNet” with some other • using AO terms and the top-level ontology notations existing systems/projects oriented to knowledge intefacilitates the process of KF, increases results reliagration is presented in Table 4. They are: bility, and minimizes possible loss of information; • translation of ontologies from modern formats • KRAFT (Knowledge Reuse and Fusion/Transformation) (RDF, DAML + OIL, etc.) into internal representais a multi-agent system for integration of heterogetion and back enables ontologies interchange with neous information systems. other OLs; • InfoSleuth is a multi-agent system for retrieving and • using the knowledge map facilitates and accelerates processing information in a network of heterogethe KS network configuration; neous information sources. Table 4 Comparison of the system “KSNet” with existing knowledge/information integration systems Characteristic

KRAFT

InfoSleuth

KSNet

Languages and formats used

KQML, P/FDM, CoLan, CIF

Supported sources

Any available information sources for which appropriate processing mechanisms exist

KQML, KIF, DAML + OIL, MS Visual C++, ILOG, MS Visual FoxPro, HTML, JavaScript Any available sources for which appropriate processing mechanisms exist

Multi-agent architecture

FIPA-based with peer-to-peer interaction

Initially KQML, KIF; currently OKBC. Initially ODBC; currently JDBC; LISP, CLISP, LDL+ Java, C/C++, NetScape Initially databases; currently any available sources for which appropriate processing mechanisms exist FIPA-based with mediating interaction

Relationships between ontologies Peculiarities

Hierarchy Processes data and constraints

Case study

Virtual enterprises

Mapping of sources ontologies to the system ontology The network of interacting agents is developed. Mechanisms of messages interchange in multi-agent systems are described Environmental date exchange network (EDEN) project

FIPA-based with mixed peer-to-peer and mediating interaction Mapping of sources ontologies onto current application ontology Utilizes object-oriented constraint networks for knowledge representation

E-business, virtual enterprises


• KSNet-approach is based on a synergistic use of knowledge from multiple sources, and provides a good basis for a personalized service of users— knowledge customers. It attempts to apply the idea of mass customization to the system “KSNet” serving their users. Knowledge Flow Reference Model [47] based on the idea of knowledge grid can introduce noticeable improvements into the KL technology. Namely, the implementation of cooperation between team members on the level of knowledge can stabilize the team’s level of knowledge by weakening the dependency of its work on its members. The knowledge flow model is oriented to propagation of knowledge within distributed cooperative teams and KL deals with satisfaction of knowledge customers’ needs. Therefore, an integration of the proposed here KSNet-approach with the idea of knowledge flow network could increase the quality of service in knowledge grid environment. The ongoing work includes: • Development of models, agent architectures and prototypes for knowledge sharing by knowledge maps, and for the management of distributed uncertain knowledge and examining the effectiveness of the proposed approach in more practical applications. • An implementation of virtual reality-based ontology engineering environment using VRML [45]. This will increase its efficiency due to combination of modeled images with the natural for human 3D perception of the world. • Investigation and development of the problemoriented agents’ negotiation/cooperation models and algorithms for KS network configuration which were earlier developed and discussed in [36]. 6. Conclusions Knowledge logistics in information grid environment is a new direction of knowledge management. The paper discusses techniques, supporting procedures/tasks used for implementation of knowledge logistics systems based on the being developed KSNetapproach. The description of multi-agent architecture of the knowledge logistics system “KSNet” based on this approach is given. The structure and major features of software prototype are presented. Carried

77

out experiments proved applicability of the developed techniques to such areas as management, product configuration, and supply chain [37]. Consequently, application of this approach could be useful for such fields as e-business, configuration management, strategic planning, etc. Utilizing ontologies and compatibility of the employed ontology notation with modern standards (such as DAML + OIL) allows seamless integration of the developed approach into existing processes in the described areas. The components of the knowledge repository enable utilizing heterogeneous knowledge sources due to the application of top-level ontology, provide scalability due to expandable/renewable internal knowledge base, and allow rapid knowledge search due to the application of the knowledge map and user profiles.

Acknowledgements Some parts of the research were done under the partner project ISTC #1993P funded by EOARD, the project #2.44 of the research program “Mathematical Modeling, Intelligent Systems and Non-linear Mechanical Systems Control” of the Presidium of the Russian Academy of Sciences, the grant #02-0100284 of the Russian Foundation for Basic Research, and the project of the research program “Fundamental Basics of Information Technologies and Computer Systems” of the Russian Academy of Sciences. References [1] ActivePerl, The industry-standard Perl distribution for Linux, 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 decomposition approach, in: Proceedings of the DARPA ISO Symposium on Advances in Enterprise Control (AEC), San-Diego, USA, 2000, pp. 1–10. [4] J.L. Aguirre, R. Brena, F.J. Cantu, Multiagent-based knowledge networks, Expert Syst. Appl. 20 (2001) 65–75. [5] H. Akkermans, OBELIX Project Proposal: The EU e-Business Ontology Project, Vrije Universiteit, 2001. http://www. ontoweb.org/workshop/ucecOntoWeb/obelix.ppt. [6] E. Bach, E. Glaser, A. Condon, C. Tanguay, DNA models and algorithms for NP-complete problems, in: Proceedings of the 11th Annual IEEE Conference on Computational Complexity (CCC’96), Philadelphia, PA, USA, 1996, pp. 290–300.

78


[7] J. Blythe, J. Kim, S. Ramachandran, Y. Gil, An integrated environment for knowledge acquisition, in: Proceedings of the 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 highperformance knowledge-based technology, in: Proceedings of 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, January 2000, p. 85. [10] COGITO: E-Commerce with Guaiding Agents Based on 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 Physical Agents (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 and implementation, in: B. Dunin-Keplicz, E. Nawarecki (Eds.), Proceedings of the Second International Workshop of Central and 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 of the Sixth International Conference on Quality Innovation Knowledge, 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-agent electronic marketplaces: analysis and classification of existing systems, in: Proceedings of the International NAISO Congress on Information Science Innovations (ISI’2001), Dubai, UAE, March 17–21, 2001. http://www.wiwi.euv-frankfurt-o.de/wiwww/de/research/project/fp softcomp veroeff pdf/Anlage203. pdf. [21] A. Maedche, S. Staab, Discovering conceptual relations from text, in: W. Horn (Ed.), Proceedings of the 14th European Conference 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 information systems based on interoperation across pre-existing ontologies, 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 communicating in agent systems: the InfoSleuth infrastructure, Technical Report MCC-INSL-056-97, Microelectronics and Computer Technology Corporation, Austin, TX, 1997. http://www. mmrg.ecs.soton.ac.uk/publications/archive/beltagy1998/beltagy1998.ps. [25] Ontoprise: Semantics for the WEB, 2001. http://www. ontoprise.de/com/start products.htm. [26] Ontolingua, Stanford University, Knowledge Systems Laboratory, 2001. http://www-ksl-svc.stanford.edu:5915/& service=frame-editor. [27] A. Pease, V. Chaudhri, F. Lehmann, A. Farquhar, Practical knowledge representation and the DARPA high performance knowledge bases project, in: A. Cohn, F. Giunchiglia, B. Selman (Eds.), Proceedings of the Conference on Knowledge Representation 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 the Stanford University School of Medicine, USA, 2000. http://protege.stanford.edu/index.shtml. [29] D.W. Rasmus, Knowledge management: more than AI but less without it, in: PC AI, vol. 14, No. 2, Knowledge Technology Inc., 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/c590gridderoure/finalsource/c590semgrid-journal-v5.doc. [31] A. Smirnov, C. Chandra, Ontology-based knowledge management for co-operative supply chain configuration, in: Proceedings of the 2000 AAAI Spring Symposium on Bringing Knowledge to Business Processes, Stanford, CA, AAAI Press, 2000, pp. 85–92. [32] A. Smirnov, M. Pashkin, I. Rakhmanova, Multi-agent WWWbased environment for distributed team decision making support, in: Proceedings of Benefit Day on Pan-European Co-operation and Technology Transfer, Budapest, Hungary, 1997, pp. 23–29. [33] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Multi-agent architecture 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 technology framework, in: Proceedings of the Sixth International Research Conference on Quality, Innovation & Knowledge Management (QIK’2002), Kuala Lumpur, Malaysia, February 17–20, 2002, pp. 936–945. [35] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Agent-based knowledge fusion in scalable information environment: major principles and system framework, in: Proceedings of the First International ICSC Congress on Autonomous Intelligent Systems (ICAIS’2002), Deakin University, 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 [36] A. Smirnov, M. Pashkin, N. Chilov, Agent architecture and negotiation protocol for knowledge logistics in distributed intelligent enterprise, in: A. Dolgui, J. Sofdek, O. Zaikin (Eds.), Production System Design, Supply Chain Management and Logistic, Proceedings of the Ninth International MultiConference on Advanced Computer Systems (ACS’2002), Part 2, Szczecin, Poland, 2002, pp. 379–386. [37] A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Knowledge source 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 information environment: concept and major technologies, in: Proceedings of the Seventh International Conference on Concurrent Enterprising (ICE’2001), Bremen, Germany, June, 2001, pp. 273–276. [39] Y. Sure, A Tool-supported methodology for ontology-based knowledge management, in: E. Stubkjaer (Ed.), The Ontology and Modeling of Real Estate Transactions in European Jurisdictions, 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., Development and deployment of a disciple agent for center of gravity analysis, in: Proceedings of the 18th National Conference on Artificial Intelligence and the 14th Conference on Innovative Applications of Artificial Intelligence, Edmonton, Alta., Canada, July 27–August 1, 2002, pp. 853–860. [41] The future of distributed middleware: grid computing and web services, 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 Knowledge 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 heterogeneity in the KRAFT project, in: Proceedings of the International Conference 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 Intelligence, MIT Press, Cambridge, MA, London, 2000, p. 8. [47] H. Zhuge, A knowledge flow model for peer-to-peer team knowledge sharing and management, Expert Syst. Appl. 23 (1) (2002). [48] H. Zhuge, A knowledge grid model and platform for global knowledge 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 Web Conference, Honolulu, Hawaii, USA, May 7–11, 2002. http://www2002.org/CDROM/poster/-53.pdf.

79

[50] H. Zhuge, Clustering soft-devices in the semantic grid, Comput. Sci. Eng. 4 (6) (2002) 60–62. Alexander V. Smirnov, Prof., received his ME, his PhD and DSc degrees in St. Petersburg, Russia, in 1979, 1984, and 1994, respectively. He is a Deputy Director for Research and a Head of Computer Aided Integrated Systems Laboratory at St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS). He is a full professor of St. Petersburg State Polytechnical University and St. Petersburg State Electrical Engineering University. His current research interests belong to areas of corporate knowledge management, multi-agent systems, group decision support systems, virtual enterprises, and supply chain management. He has published more than 150 research papers in reviewed journals and proceedings of international conferences, books, and manuals. Mikhail P. Pashk received his MAM from St. Petersburg State University, Russia, in 1992. He is a researcher at Computer Aided Integrated Systems Laboratory of SPIIRAS. His current research interests include multi-agent systems, ontology engineering and group decision support systems. He has published more than 40 research papers in proceedings of international conferences, books and manuals. Nikolai Chilov received his ME in St. Petersburg State Technical University, Russia, in 1998. He is a researcher at the Computer Aided Integrated Systems Laboratory of SPIIRAS. His current research interests belong to areas of virtual enterprise configuration, supply chain management, knowledge management, ontology engineering and multi-agent systems. He is an author/co-author of more than 30 research papers published in proceedings of international conferences and books. Tatiana Levashova received her ME degree at St. Petersburg State Electrical Engineering University in 1986. She is a leader programmer at Computer Aided Integrated Systems Laboratory of SPIIRAS. Her current research is devoted to knowledge-related problems such as knowledge representation, knowledge management, and ontology management. She has published more than 15 papers in reviewed journals and proceedings of international conferences.