From: HCI-02 Proceedings. Copyright © 2002, AAAI (www.aaai.org). All rights reserved.
A KSNet-Approach to Knowledge Logistics in Distributed Environment
Alexander Smirnov1, Michael Pashkin1, Nikolai Chilov1,
Tatiana Levashova1, Fred Haritatos2
1
St.Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences
39, 14th Line, St.Petersburg, 199178, Russia
smir@mail.iias.spb.su
2
Information Directorate of the Air Force Research Laboratory
32 Brooks Rd., Rome, NY, USA
fred.haritatos@rl.af.mil
Abstract
Increases in the amount of required information and intensive cooperation have caused a need to create an efficient
knowledge sharing and exchange between members of the
global information environment. The study of operations
constituting these activities has led to the formulation of a
new scientific area called knowledge logistics. This paper
presents a KSNet-approach to knowledge logistics based on
knowledge fusion, which implies a synergistic use of
knowledge from different sources in order to complement
insufficient knowledge and obtain new knowledge. This approach utilizes such technologies as ontology management
and intelligent agents. Possible Air Force application areas
are discussed.
Introduction
Rapid development of the Internet has allowed users to be
exposed to a large amount of information about different
problem areas. Simultaneously, the number of information
sources has constantly grown and information search engines have improved. Algorithms for data searching in
large databases, tools for heterogeneous data maintenance,
technologies of data storing and representation, and systems dealing with data searching and retrieving are widespread. Today, in virtually all domains, data exists that
could open the door to widespread problem resolution:
however, just possessing the data is not enough.
For opportunities to be taken advantage of and for problems to be resolved even before they become problems,
more data is not needed. Instead an efficient management
of the information/knowledge is needed, which must be
pertinent, clear, and correct, and it must be timely processed and delivered to appropriate locations, so that it
could facilitate global awareness. Without this level of efficiency, it is nearly impossible to predict developments or
foresee problems in the everchanging and increasingly
Copyright © 2002, American Association for Artificial Intelligence
(www.aaai.org). All rights reserved.
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complex knowledge base that constitutes our current, scientific world.
In order for this to happen, intensive cooperation and
open real-time knowledge exchange between participants
in the so-called “global information environment” are required, so that the right knowledge from distributed
sources can be integrated and transferred to the right person within the right context, at the right time, for the right
purpose. The aggregate of these interrelated activities will
be referred to as Knowledge Logistics (KL) (Smirnov et al
2002). The KL is based on individual user requirements,
available knowledge sources (KSs), and content analysis in
the information 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 conducting of complex operations, and deliver results to the users/knowledge customers
in a personalized way. An efficient approach is required,
which could make possible for the data to become information that leads to knowledge and to sophisticated understanding (Figure 1).
Described here is an approach that addresses KL through
knowledge fusion (KF). KF implies synergistic use of
knowledge from different sources in order to complement
insufficient knowledge and obtain new knowledge (Smirnov et al 2002). This approach is still under development
and has not been putted to exhaustive tests.
The Air Force as a Case Study
The possible application domains of this research belong to
the following areas:
• Large-scale air operations and/or joint aerospace operations based on large-scale dynamic systems (enterprises)
supporting distributed operations in an uncertain and
rapidly changing environment. Here the information
collection, assimilation, integration, interpretation, and
dissemination are needed ( Adams et al 2000), (Howells
et al 1999).
• Focused logistics operations and/or Web-enhanced logistics operations addressing sustainment, transporta-
Data
tion and end-to-end rapid supply to the final destination.
Here distributed information management and real-time
information/knowledge fusion to support continuous information and knowledge integration of all participants
of the operations are needed (DARPA 2002).
• Partnerships among industrial and defense-related (command, control and intelligence) organizations. Here dynamic identification and analysis of information sources
and providing for interoperability between market participants (players) in a semantic manner are needed
(Cunningham 2001, Kim et al 1997, Kurbel et al 2001).
• War avoidance operations such as peace-keeping, peaceenforcing, non-combatant evacuation or disaster relief
operations. In classical war operations the technology of
control is strictly hierarchical, unlike Operations Other
Than War, which are very likely to be based on the cooperation of a number of different, quasi-volunteer,
vaguely organized groups of people, non-government
organizations, institutions providing humanitarian aid
but also military personnel and official governmental
initiatives. Here many participants will be ready to share
information and knowledge with some well specified
community (Pechoucek et al 2001).
For all of these areas it is possible to describe management (command and control) systems as an organizational
combination of people, technologies, procedures and information/knowledge. People in an organization share
knowledge of their relationships through the delegation of
tasks, transferring of information, obligation, synchronization of tasks, etc. Here management systems support the
Observe-Orient-Decide-Act loop that characterizes the Air
Force command and control cycle (Voort 2000)).
K
D
I
E
C
Knowledge Bases
Experts
Repositories
Tools
Figure 1. Conceptual framework of information support
(adapted from (Anken 2002))
H
F
A
Knowledge
Information
G
B
Understanding
Information
Transformation Process
Elements of the
Network
G
B
G
H
D
X
Outsourcing
I
Competitors
B
A
G
B
D
A
I
E
C
Partner 1
K
E
E
Unit 1
C
H
F
K
A
K
A
D
I
Unit n
…
Unit 2
HG
B
F
F
D
I
C
Partner 2
K
E
I
Partner n
Figure 2. Knowledge source network (adapted from (Golm et al))
a customer-oriented knowledge model based on a fusion of
knowledge acquired from the underlying KSNet units/
knowledge sources (KSs) constituting the lower level and
containing their own knowledge models.
The main principles considered during development of
this approach and a KF system based on it originated from
the characteristics of modern “e”-applications. These applications widely use ontologies as a common language for
business process / enterprise modeling (Goossenaerts et al
2001, O’Leary 2000, URL OIL Ed. 2002, URL Protégé
2002, Smirnov 2001). Thus, this approach focuses on utilizing reusable knowledge through ontological descriptions
(Guzrino 1998), with an object-oriented constraint network
paradigm being considered as a common knowledge notation (Smirnov et al 2001). This perspective of knowledge
representation correlates well with the semantic metadata
representation concept developed under the Semantic Web
project URL Semantic Web 2002).
The application of intelligent agents representing their
knowledge via ontologies (Weiss 2000) was motivated by
the KF system's need for flexibility, scalability, and customizability. A multi-agent system architecture, based on
the FIPA Reference Model (FIPA URL 2002) was chosen
as a technological basis for the definition of agents’ properties and functions.
Knowledge Fusion System “KSNet”
KSNet-Approach
The approach presented here is based on the assumptions
that (i) knowledge as a complex resource is characterized
by structure, cost, location, access time and life-time, and
(ii) a knowledge worker is an owner of knowledge and a
member of a team/group.
The KL problem in the KSNet-approach is considered as
a network configuration that includes end-users/customers,
loosely coupled knowledge sources/resources, and a set of
tools and methods for knowledge processing. The network
of loosely coupled sources located in the information environment will be referred to as the knowledge source network or “KSNet” (Figure 2). The upper level represents
A General Scenario
The operational principles and the architecture as well as a
research prototype of the KF system called “KSNet” have
been developed to illustrate the KSNet-approach.
The system architecture (Figure 3) takes into account
such modern requirements to e-business applications as
(i) flexibility, (ii) learning from the user, (iii) integrity,
(iv) velocity, (v) open connectivity, (vi) reasoning and
(vii) customizability.
The system works in terms of a common Application
Ontology (AO) that describes a problem domain and is
stored in an ontologies library. The AO is based on domain, tasks and methods ontologies, which are also stored
HCI-Aero 2002
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Knowledge Source Ontology
Parametric
Constituent
Request Ontology
User Request
Structural
Constituent
User
Constituent
Answer
User
Profile
User – Logistics
Manager
Application
Ontology
Constituent
Knowledge
Source
Constituent
Correspondence
Instances
User – Engineer
Request
Ontology
Domain Ontology
Application
Ontology
Constituent
Knowledge Source
Request
Ontology
User – Operational
Manager
Knowledge
Map
Correspondence
Application
Ontology
User
Profile
User
Request
Processing
Tasks & Methods
Ontology
User
Profile
Passive Sources
Databases,
Knowledge Bases,
Documents,
etc.
Active Sources
Experts,
Knowledgebased tools,
etc.
Figure 3. Conceptual scheme of the user-oriented ontology-driven KF methodology for a supply chain case study
in terms of an associated expandable user request ontology
and thereby with a part of the AO pertinent to the user/ user
group. User profiles are used during interactions to achieve
efficient personalized service. Every user request consists
of two parts: (i) a structural constituent (containing the request terms and relations between them), and (ii) a parametric constituent (containing additional user-defined constraints). An auxiliary KSNet configuration defining when
and what KSs are to be used would be built to maximize
the efficiency of. request processing. For this purpose, a
knowledge map including information about locations of
KSs is used. Translation between the system’s and KS’
notations & terms is performed using KS ontologies.
edge sources), facilitator (“yellow pages” directory service
for the agents), mediator (task execution control), and user
agent (interaction with users). The following problemoriented agents specific for KL and scenarios for their collaboration were developed: translation agent (terms translation between different vocabularies), KF agent (operation
performance for KF), configuration agent (efficient use of
KSNet), ontology management agent (ontology operations
performance), expert assistant agent (interaction with experts), and monitoring agent (knowledge sources verifications). The system users and agents are shown in Table I.
The main technological issues related to the system’s
functioning are shown in Figure 4. An ontology-based
knowledge representation is described in the next section.
Users of the System
The following classes of users of the system "KSNet" were
defined: (i) knowledge consumers making requests for
knowledge search/generation by the system, (ii) experts
studying and describing problem domains and entering
new knowledge, (iii) knowledge engineers validating
knowledge and facilitating its entry by the experts,
(iv) ontology engineers validating ontologies and facilitating operations with them, (v) software engineers performing system operation related functions (adaptation, creation, and connection of methods for task solving, parsing
and translation of attribute values obtained from KSs; connection of new KSs, etc.), (iv) administrator performing
system diagnostics, user rights management, etc.
To facilitate interactions with the users the technology of
user profiling is used. User Profile is an organized information storage about the user, his/her requests history, etc.
This allows faster search due to analyzing and utilizing request history and user preferences, Just-Before-Time request processing, etc.
Multiagent Architecture
A multi-agent architecture for the “KSNet” includes two
types of agents. FIPA-based technological kernel agents
used in the system are: wrapper (interaction with knowl-
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Ontology-Based Knowledge Representation
Knowledge Representation Formalism
A formalism of object-oriented constraint networks has
been chosen for ontology representation. An abstract
KSNet model is based on this formalism. This abstract
model unifies the main concepts of standard object oriented
and constraint programming languages and . It supports the
declarative representation, efficiency of dynamic constraint
solving, and problem modeling capability, maintainability,
reusability, and extensibility of the object-oriented technology.
According to the above described formalism ontology
(A) is defined as: A = (O, Q, D, C) where
O – a set of or object classes (“classes”). Each of the
entities in a class is considered as an instance of the class.
Q – a set of class attributes (“attributes”).
D – a set of attribute domains (“domains”).
C – a set of constraints. Six types of constraints have
been defined: (i) accessory of attributes to classes,
(ii) accessory of domains to attributes, (iii) compatibility of
classes (compatibility structural constraints)
(iv) hierarchical relationships (hierarchical structural,
Table I. Users and agents in the KSNet-approach
User of the system «KSNet»
Ontology engineers
Software engineers
Knowledge engineers
Experts
Knowledge consumers
Administrator
Agents
•
User agent
•
Ontology management agent
•
Monitoring agent
•
Facilitator
•
Mediator
All agent types
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
User agent
Expert assistant agent
Mediator
Monitoring agent
Facilitator
Translation agent
Ontology management agent
Monitoring agent
Mediator
Facilitator
Expert assistant agent
User agent
User agent
Mediator
Monitoring agent
Facilitator
Translation agent
Ontology management agent
Wrapper
Configuration agent
KF agent
Monitoring agent
constraints) “is a” defining taxonomy of classes (type=0),
and “has part”/“part of” defining hierarchy of classes
(type=1), (v) associative relationships (“one-level” structural constraints), and (vi) functional constraints.
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
Figure 4. Main system tasks and techniques
Agent interactions with user
Agents support work of ontology engineers: help them
on the preparation phase to import and to build ontologies
Agents use methods developed by the software engineers. The wrappers are created by the software engineers
Agents support the process of direct knowledge entry
into the internal knowledge base
Agents support the process of alternative KSs ranking
Agents support the process of requests
processing
Agent provides for the results of KS monitoring
Knowledge Representation Technology
Since knowledge that can be acquired from such sources as
KBs, DBs, documents, etc., is limited the value of expert
knowledge based on experts' experience and skills is very
high. As a result it is necessary for KM system to have
mechanisms (interfaces, techniques, models, scenarios and
architecture) for efficient acquisition and revealing of expert knowledge, and the mechanisms of knowledge representation (Aamodt, 1994).
In accordance with up-to-date technologies and standards, the information kernel for knowledge management
is built as shown in (Figure 5). As described above, knowledge is represented by an 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 on the basis of this notation. Access to the database
is performed via ODBC as a standard data access mechanism under MS Windows OS. Remote access to the knowledge is performed via common HTTP Internet protocol.
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Representation is done via either interactive
HTML+VRML Java enabled pages for users or
DAML+OIL-based format for knowledge-based tools. Client-server software architecture is shown in Figure 6.
The application of virtual reality for ontology engineering increases its efficiency due to a combination of modeled images with our natural 3D perception of the world.
Figure 7 demonstrates a sequence of prototyped VRMLbased screens for “in-depth” search from transportation vehicle taxonomy to helicopter structure and collaborative
browsing for a case of two experts.
HTTP Requests
HTML Pages
ISAPI
MS Internet
Explorer as a
thin client
Internet or Intranet
HTTP Requests
Partners &
Similar Systems
DAML+OIL Files
Web Server
(MS Internet
Information
Server or MS
Personal Web
Server)
User
Interface
Part of
System
“KSNet”
ODBC
Internal
Information
Storage
Figure 6. Client-server user interface architecture
Ontology
Management
User
Interface
DAML+OIL
HTML / VRML
XML
RDF
Data
Management
Representation
ISAPI / CGI
ODBC
Implementation
Internal Object Scheme
Classes
Attributes
Constraints
Domains
Object-Oriented Constraint Networks
DAML - The DARPA Agent Markup Language
OIL – The Ontology Inference Layer
RDF – Resource Description Framework
XML – eXtensible Markup Language
HTML – Hyper Text Markup Language
Notation
VRML – Virtual Reality Marjup Language
ISAPI – Internet Server Application
Programming Interface
CGI – Common Gateway Interface
ODBC – Open DataBase Connection
Figure 5. Standards of knowledge management
information kernel
Conclusions
In the face of the increasing amount of required information/knowledge and intensive cooperation, knowledge logistics could be very powerful for collaboration between
members of joint actions and operations found in many industrial and military applications.
The main advantages of the KSNet approach include the
following: (i) agent-based architecture increases scalability,
efficiency and interoperability of the system “KSNet”; (ii)
using ontologies libraries with top-level ontology facilitates
problem domain description; (iii) application ontology
terms and top-level ontology notation facilitate knowledge
fusion, minimize possible loss of information, increase reliability, etc.; (iv) utilizing user profiles and request ontologies increases customizability and velocity; (v) translation of ontologies from advanced formats (e.g.
DAML+OIL) into internal representation and back enables
ontology interchange and sharing; and (vi) application of
constraint networks allows rapid problem manipulation by
adding/changing/ removing components (objects, constraints, etc.).
The research project is still in its early stages. Planned
work includes research devoted to negotiation issues and
knowledge modeling, particularly, incorporation of fuzzy
models, and prototyping and intensive experimentation.
Acknowledgments
This research was partially funded by the EOARD of the
USAF and by a grant from the Russian Foundation for Basic Research. The authors are grateful to the US AFOSR
for the editorial support provided.
Expert 1
Figure 7. Example of VRML-based screenshots and collaborative browsing
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Expert 2
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