Semantic and Agent Technologies in
Developing Distributed Applications
Resource
Agent
“Device”
Resource
Agent
“Expert”
Resource
Agent
“Service”
Vagan Terziyan
[email protected]
IT2007, Jyvaskyla, Finland, 01.11.2007
Industrial Ontologies Group
Contents
•
•
•
•
•
•
Future Web –related trends;
Motivation for agent technology;
Motivation for semantic technology;
Vision for future distributed applications;
Some relevant projects and activities
Case: “Ontology-Based Portal for Management
of National Educational Resources” (from
Ukraine);
• Presentation slides can be downloaded from:
 http://www.cs.jyu.fi/ai/Terziyan_Klymova.ppt
Three alternative trends of Web development
Human
Communities
Machines,
devices,
computers
Facilitates
Human-toHuman
interaction
Applications,
services, agents
Facilitates
Machine-toMachine
interaction
Facilitates
Software-toSoftware
interaction
Challenges of Distributed Systems
•
Growing complexity and heterogeneity of computer
systems and networks used in industry  need for new
approaches to manage and control them
•
IBM vision: Autonomic computing – Self-Management
(includes self-configuration, self-optimization, selfprotection, self-healing)
•
Ubiquitous computing, “Internet of Things”  huge
numbers of heterogeneous devices are interconnected
• “nightmare of pervasive computing” when almost impossible
to centrally manage the complexity of interactions, neither even to
anticipate and design it.
•
•
We believe that self-manageability of a complex system
requires its components to be autonomous themselves, i.e.
be realised as agents. Agent Technology in SE is also
considered to be facilitating the design of complex systems
We also believe that interoperability among heterogeneous
components can be realized by utilization of Semantic
Technology.
Agent Definition [IBM]
Semantic Web
“The Semantic Web is a vision: the idea of having data on the
Web defined and linked in a way that it can be used by
machines not just for display purposes,
but for automation, integration and reuse
of data across various applications”
http://www.w3.org/sw/
The Semantic Web is an initiative with the goal of extending the
current Web and facilitating Web automation, universally accessible
web resources, and the 'Web of Trust', providing a universally
accessible platform that allows data to be shared and processed by
automated tools as well as by people.
Semantic Web: New “Users”
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
L o gical S upp o rt
L an gu ag es
T o ols
A p plications /
S ervices
S em a n tic
W eb
WWW
and
B ey o n d
C reators
U sers
W eb content
Semantic Web: Annotations
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
L o gical S upp o rt
S em a n tic
W eb
L an gu ag es
WWW
and
B ey o n d
T o ols
C reators
W eb content
Semantic annotations are
specific sort of metadata,
A p plications /
S ervices which provides information
about particular domain
objects, values of their
properties and relationships, in
U sers
a machine-processable, formal
and standardized way.
Semantic Web: Ontologies
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
L an gu ag es
T o ols
S em a n tic
W eb
WWW
and
B ey o n d
C reators
Ontologies make metadata
interoperable and ready for efficient
L o gical
S upp o rt and reuse. It provides
sharing
shared and common understanding
of a domain, that can be used both
by people and machines.
A p plications /
S ervices
U sers
W eb content
Semantic Web: Rules
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
S em a n tic
W eb
L an gu ag es
WWW
and
B ey o n d
T o ols
C reators
W eb content
L o gical S upp o rt
Logical support in form of rules is needed to infer
implicit content, metadata and ontologies from the
explicit ones.
Rules can act as a means to draw
A p plications /
inferences, toS ervices
configure systems, to express
constraints, to specify policies, to react to
events/changes, to transform data, to specify
behavior of agents, etc.
U sers
Semantic Web: Languages
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
L o gical S upp o rt
S em a n tic
W eb
L an gu ag es
WWW
and
B ey o n d
C reators
Languages areAneeded
for machine-processable
p plications /
descriptions
of: metadata (annotations) like e.g.
S ervices
RDF; ontologies like e.g. OWL.; rules like e.g. RuleML.
The challenge is to provide a framework for specifying
the syntax (e.g. XML) and semantics of all of these
U sers
languages in a uniform
and coherent way.
T o olsformal
W eb content
Semantic Web: Tools
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
S em a n tic
W eb
L an gu ag es
WWW
and
B ey o n d
T o ols
C reators
L o gical S upp o rt
User-friendly tools are needed for
metadata manual creation (annotating
content)
or /automated generation, for
A p plications
S ervicesengineering and validation, for
ontology
knowledge acquisition (rules), for
languages parsing and processing, etc.
U sers
W eb content
Semantic Web: Applications and Services
S em a n tic
W eb a n d
B ey o n d
U sers
C reators
applications
S em antic W eb
content
agents
S em an tic
A n no tations
O n to log ies
L o gical S upp o rt
L an gu ag es
T o ols
A p plications /
S ervices
S em a n tic
W eb
WWW
and
B ey o n d
C reators
W eb content
Utilization of Semantic Web
metadata, ontologies, rules,
U sers
languages and tools enables to
provide scalable Web applications
and Web services for consumers and
enterprises" making the web 'smarter'
for people and machines.
Semantic Technology
• Semantic Web itself is not the main goal;
• “Semantic” is a useful feature of an application;
• Semantic Technology is efficient tool to design
applications and make them smart, flexible and
interoperable;
• Combination with modern trends like e.g. Mobile
and Ubiquitous Computing, Embedded Systems;
Web Services and SOA, Agent Technologies and
MAS; Machine Learning, data Mining and
Knowledge Discovery; Distributed, Autonomous
and Self-Configurable Architectures, Grid
Computing, P2P; etc. makes advantages
provided by Semantic Web more visible.
Semantic Web: which resources to annotate ?
This is just a small part of
Semantic Web concern !!!
Technological
and business
processes
External world
resources
Web resources /
services / DBs / etc.
Semantic
annotation
Shared
ontology
Multimedia
resources
Web users
(profiles,
preferences)
Web access devices and
communication networks
Smart
machines,
devices,
spaces, etc.
Web agents /
applications /
software
components
GUN Concept [Industrial Ontologies Group]
GUN – Global
Understanding
eNvironment
GUN
=
Global Environment
+
Global Understanding
=
Proactive Self-Managed
Semantic Web of Things
= (we believe) =
“Killer Application” for
Semantic Web Technology
GUN and Ubiquitous Society
GUN can be considered as
a kind of Ubiquitous EcoSystem for Ubiquitous
Society – the world in
which people and other
intelligent entities
(ubiquitous devices, agents,
etc) “live” together and
have equal opportunities
(specified by policies) in
mutual understanding,
mutual service provisioning
and mutual usability.
Human-to-Human
Human-to-Machine
Machine-to-Human
Machine-to-Machine
Agent-to-Agent
Resources in GUN (1)
• Software: Software and software components,
operation systems, tools, Web-services, etc.;
• Data: Electronic documents, warehouses,
databases, histories, diaries, lifeblogs, digital
media resources, etc.
• Devices: all kind of devices, machines, sensors,
actuators, adapters, communicators, switches,
routers, etc. and their components;
• Humans: Users, customers, service providers,
buyers, sellers, workers, operators, experts, etc.;
• Communication systems and networks: PANs,
LANs, MANs, WANs, RFIDs, WiFi, WiMax, LTE, etc.
Resources in GUN (2)
• Organizations: various compositions of various
resources selected and integrated for certain
purpose, e.g. companies, universities, networks, etc.;
• Processes: natural, controlled or goal-driven,
dynamics of organizations;
• Concepts, Models and Ontologies: various
concepts, models and ontologies, which formally
describe various resources, their organizational
structure, dynamics and coordination;
• Messages: all kind of messages various resources
exchange among themselves during their lifecycle;
• Standards: all kind of standards according to which
resources are produced, described, used, operate,
communicate etc.
The Roadmap towards GUN
Qualitative transitions
Adaptation and
personalization
Core DAI
platform
Coordination
and
networking
Proactivity
and
behavior
Security
and trust
Autonomicity, selfmanagement, selfconfigurability
Metadata,
Industrial cases
semantics,
implementation
ontologies
Intelligence,
Human…
learning,
centricity,
reasoning,
GUI, Web
planning
2.0, Wiki
SmartResource project summary
• SmartResource Tekes project (2004-2007):
http://www.cs.jyu.fi/ai/OntoGroup/projects.htm .
• One of the most essential results of the
SmartResource project was creation of the
“Smart Resource Technology” for designing
complex software systems.
• The technology benefits from considering each
traditional system component as a “smart
resource”, i.e. proactive, agent-driven, selfmanaging.
Challenge 1: General Adaptation Framework
SC
Universal reusable
semantically-configurable
adapters
Challenge 2: General Proactivity Framework
Role
“Feeder”
description
Role
“SCADA”
description
Role
“Maintenanc
e worker”
description
GB
Universal reusable
semantically-configurable
behaviors
Challenge 3: General Networking Framework
Scenario
“Predictive
maintenance”
description
PI
Scenario
“Data
integration”
description
Universal reusable
semantically-configurable
scenarios for business
processes
UBIWARE: “Smart Semantic Middleware
for Ubiquitous Computing”
• Funded by Tekes;
• In the Web: http://www.cs.jyu.fi/ai/OntoGroup/projects.htm
• Started: 1 July 2007;
• Summary:

UBIWARE project will be build on the foundation laid in the
SmartResource project. It aims at designing a new generation
middleware platform (UBIWARE) which will allow creation of
self-managed complex industrial systems consisting of mobile,
distributed, heterogeneous, shared and reusable components
of different nature.
• Partners:

IOG (AC, UJ), ABB, Fingrid, Hansa Ecura, Inno-W, Metso
Automation, Metso Shared Services
PRIME: “Proactive Inter-Middleware for
Integrating Embedded and Enterprise Systems”
• EU FP7 STREP proposal for the objective ICT-2007-3.7:
“Networked Embedded and Control Systems” ;
• Submitted: 8 October 2007;
• Summary:
 The technological goal of the project is a PRIME intermiddleware which will connect industrial resources belonging
to different layers through the middleware platforms that are
normally used for connecting resources at the respective
individual layers. Interoperability of resources of this level of
heterogeneity requires wide utilization of semantic
technologies to provide cross-layer communication services
(data-level interoperability) to the resources and multi-agent
technologies to provide collaboration-support services
(functional protocol-level interoperability) for these resources.
•
Partners: IOG (University of Jyvaskyla, Coordinator), Free University of
Amsterdam, University of Athens, 4 international companies
Inter-Middleware for Intra-Enterprise
Resource Integration
Layer n middleware
…
Layer 3 middleware
Layer 2 middleware
Layer 1 middleware
Existing tools, middleware and and platforms for resource
mediation, interoperability, integration, collaborative work, etc.
PRIME project
Repository
of roles and
scenarios
Repository
of atomic
behaviors
Directory
How to make university
resources interoperable?
?
University Resources
Centralized data and metadata
Ontology
Metadata
Metadata
Metadata
University Resources
Distributed data and centralized
metadata
Ontology
Metadata
Metadata
Metadata
University Resources
Distributed data and metadata
Ontology
Metadata
Metadata
Metadata
University Resources
Ontology-Based Portal for National Educational
and Scientific Resources Management
Masha Klymova
Kharkiv National University of Radioelectronics, Ukraine
Actual problems
• AMBIGUITY: There is a problem of ambiguous information about
educational and scientific resources
– different information about the same resources is being managed by different
organizational units;
• INCONSISTENCY: Inconsistency of the parameters of the resources is the
result of permanent uncontrolled updates of the information about the
resources without synchronization;
• LACK of TRUST: Reported parameters’ values are not easily verified of their
correctness (it takes much time and human resources as the procedure is
not automated);
• LACK of FLEXIBILITY: The structure of the educational and scientific
processes in organization is permanently developing and this often leads to
a cardinal changes of the appropriate ICT infrastructure.
Solution
Creation of a flexible, standardized and personalized, secure, web-oriented
information/knowledge management system for academic resources.
Such system will be organized as Ontology-Based Portal for National
Educational and Scientific Resources Management
Academic resources are:
– Organizations (Ministry, universities, institutes, schools, faculties, departments etc);
– Academic documents (study programs, curricula, recommendations, instructions,
manuals, textbooks, etc;
– People (administration, teachers, researchers, students, etc);
– Facilities (classes, computers, equipments, etc)
– Scientific products (dissertations, papers, degrees)
– Processes and activities (lectures, conferences, workshops, testing, etc)
– Etc… all entities related to science and education and appeared in reporting
documents from educational establishments
Use case scenario (1)
• Wanted: to rank national Universities according to e.g.
following criteria:
– (A) Amount of full-time professors per student;
– (B) Amount of papers published in international journals per
professor;
– (C) Amount of computer classes per student
• using ranking formula e.g.:
Rank = 0.4 * A + 0.5 * B + 0.1 * C
• and provide transparency of ranking procedure and results
Use case scenario (2)
• Universities register each new resource online at
the portal in appropriate class of the resources in
the ontology:
–
–
–
–
–
professor;
student;
journal paper with the link to publisher;
computer class;
etc.
Use case scenario (3)
• Ministry of Education (for example) creates (through
appropriate interface of the portal) new criteria for the
universities ranking:
– (A) Amount of full-time professors per student;
– (B) Amount of papers published in international journals per
professor;
– (C) Amount of computer classes per student
• and these criteria will be automatically transformed to the
appropriate formal queries to registered resources at the
portal. Based on calculations on queries output results, the
values to the new criteria will be obtained, saved and
constantly updated in the portal for each university.
Use case scenario (4)
• Ministry of Education also creates (through appropriate
interface of the portal) new formula for university ranking
based on the criteria:
Rank = 0.4 * A + 0.5 * B + 0.1 * C
• and this formula will be automatically transformed to
appropriate formal procedure applied to each university
and their valid criteria values. Based on calculations the
values of current ranks will be obtained, saved and
constantly updated in the portal for each university and
each registered ranking procedure.
Use case scenario (5)
• Transparency of the procedure:
• Anyone who has access to the resources registered at the
portal can easily check content behind every value of every
parameter of every criteria of every university
• For example if the value of parameter:
– amount of journal papers in international publishers = 35
• Then by clicking 35 one obtains the list of the papers with
reference to the publisher; further clicks result to
opportunity to see full text paper and the publisher web
page, etc.
Other possible scenarios
•
•
•
•
•
•
Ranking
Accreditation
Licensing
Monitoring
Reporting
…
Modules of the system
Ontology store
system
The interface making module
The Web-interface
making system
The reports
making system
Ontology server
System's kernel
Access control system
Audit system
Common managing system
Tasks module
Resources` registration
Accreditation
Ranking
The ontological
knowledge base
structure
The interface structure
Task area
(e.g. “Resource registration”)
The objects type
selection area
(e.g. “Department”)
The object filter
(e.g. “Kharkov University”)
The document and
parameters type
selection area
e.g. “Contingent of students”
Content area
Tips area
Properties of the resources
Each resource registered at the portal may contain
properties of two types:
– computable (their value is calculated automatically
based on the description joined to the field and can not
be modified manually);
• For example, amount of full professors, average age of
personnel, etc.
– atomic (their values are being set evidently pointing out
at some information resource or literal).
• For example, name of department, title of paper, telephone
number etc.
Computable properties
• The computable properties are divided into 3 types:
– The fields containing the summing operation;
– The fields containing a typical formula;
– The combined fields;
• For each of the types its own information
presentation system is performed.
Ongoing projects
• EU Tempus Tacis SCM Project T020B06 (2007-2008)
Title: “Towards Transparent Ontology-Based Accreditation”
• Ministry of Science and Education of Ukraine project (2006 - …)
Title: “Ontology-Based Portal for Management and Evaluation of National
Scientific and Educational Resources”
More details about Ukrainian portal
• Full presentation about the portal:
– http://www.cs.jyu.fi/ai/OntoPortal-2007.ppt
• Seminar with the portal presentation:
– Tomorrow, 2 November, 13:00, University of Jyvaskyla,
Agora (Mattilanniemi 2), Room C 301 (“Aquarium”)
Summary
“Ask not what the Semantic Web
Can do for you, ask what you can
do for the Semantic Web”
Hans-Georg Stork, European Union
http://lsdis.cs.uga.edu/SemNSF
• Presentation slides can be downloaded from:
– http://www.cs.jyu.fi/ai/Terziyan_Klymova.ppt
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Semantic and Agent Technologies in Developing …