SmartResource Project: industrial case for Semantic Web and Agent Technologies

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SmartResource Project (industrial case for
Semantic Web and Agent Technologies)
http//www.cs.jyu.fi/ai/OntoGroup/SmartResource_de
tails.htm

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Industrial Ontologies Group
GROUP PROFILE

• Semantic Web and Ontologies
• Web Services and Semantic Web Services
• (Multi) Agent Technologies
• Distributed Artificial Intelligence
• Knowledge Management
• Ubiquitous Computing
• Mobile Context-Aware Services and Applications
• Machine Learning, Data Mining and Knowledge
  Discovery

Industrial Ontologies Group
http//www.cs.jyu.fi/ai/OntoGroup/
The main objective of the group is to contribute
to fast adoption of Semantic Web and related
technologies to local and global industries. It
includes research and development aimed to design
a Global Understanding Environment as next
generation of Web-based platforms by making
heterogeneous industrial resources (files,
documents, services, devices, business processes,
systems, organizations, human experts, etc.)
web-accessible, proactive and cooperative in a
sense that they will be able to automatically
plan own behavior, monitor and correct own state,
communicate and negotiate among themselves
depending on their role in a business process,
utilize remote experts, Web-services, software
agents and various Web applications.
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IOG cooperates with different units of Jyvaskyla
University and performs the activities in the
domain Industrial Applications of Semantic Web
in Finland
MIT Department
Agora Center
TITU
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GUN Concept
GUN Global Understanding eNvironment
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S m a r t R e s o u r c e
Tekes Project (2004-2006)
WIDER OBJECTIVE
- to combine the emerging Semantic Web, Web
Services, Peer-to-Peer, Machine Learning,
Ubiquitous Intelligence and Agent technologies
for the development of a global GUN-based EAI
Platform and smart e-maintenance environment, to
provide Web-based support for the predictive
maintenance of industrial devices by utilizing
heterogeneous and interoperable Web resources,
services and human experts
Project results in the Web http//www.cs.jyu.fi/a
i/OntoGroup/SmartResource_details.htm
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Smart Maintenance Environment
Experts
Devices with on-line data
exchange
data
Maintenance
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SmartResource

• SmartResource GUN restricted by Maintenance
  Domain
• Interoperability (1st year)
• Maintenance ontology
• RSCDF for dynamic and context-sensitive resource
  metadata
• Semantic Adapters for heterogeneous resources
• Automation (2nd year)
• Agent platform for a resource
• RGBDF for ontological modeling of a resource
  proactive behavior in a business process
• RGBDF engine for an agent to run simple
  (individual) business process
• Integration (3rd year)
• Multiagent platform for business process
  integration
• RPIDF for ontological modeling of complex
  business processes
• RPIDF Engine for business process integration
• Industrial Cases ABB, Metso Automation.

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Dimensions of RDF Development in SmartResource
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Roles of a Resource and RDF Support
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Future of Smart Maintenance Environment
exchange
data
Maintenance
On-line learning
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Obtain More Information about SmartResource from
Head of SmartResource Industrial Consortium
(Steering Committee Head) Dr. Jouni Pyötsiä,
Metso Automation Oy. Jouni.Pyotsia_at_metso.com ,
Tel. [phone]
SmartResource Contact Person Prof. Timo Tiihonen,
Vice-Rector, University of Jyväskylä tiihonen_at_it.j
yu.fi , Tel. [phone]
SmartResource Project Leader Prof. Vagan
Terziyan, Agora Center, University of
Jyväskylä vagan_at_it.jyu.fi , Tel. [phone]
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Semantic Web Future Research Directions

• Vagan Terziyan

Industrial Ontologies Group
Galway, DERI, 28 April 2006
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Four Years Ago Six Challenges for the Semantic
Web
by Richard Benjamins, Jesus Contreras, Oscar
Corcho, Asuncion Gomez-Perez
Challenge 1 Availability of Content
Challenge 2 Ontology Availability, Development
and Evolution
Challenge 3 Scalability of Semantic Web Content
Challenge 4 Multilinguality
Challenge 5 Visualization
Challenge 6 Semantic Web Language Standardization
How well do we proceed ?
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Vision 2006 Real Semantic Web

• Semantic data generation vs. reuse (the ability
  to operate with the semantic data that already
  exist, i.e. to exploit available semantic
  markup)
• Single-ontology vs. multi-ontology systems (the
  ability to operate with huge amounts of
  heterogeneous data, which could be defined in
  terms of many different ontologies and may need
  to be combined to answer specific queries)
• Openness with respect to semantic resources (the
  ability to make use of additional, heterogeneous
  semantic data, at the request of their user)
• Scale as important as data quality (the ability
  to explore, integrate, reason and exploit large
  amounts of heterogeneous semantic data, generated
  from a variety of distributed Web sources)
• Openness with respect to Web (non-semantic)
  resources (the ability to take into account the
  high degree of change of the conventional Web and
  provide data acquisition facilities for the
  extraction of data from arbitrary Web sources)
• Compliance with the Web 2.0 paradigm (the ability
  to enable Collective Intelligence based on
  massively distributed information publishing and
  annotation initiatives by providing mechanisms
  for users to add and annotate data, allowing
  distributed semantic annotations and deeper
  integration of ontologies
• ?Open to services (the ability applications
  integrate Web-service technology in applications
  architecture).

Motta and Sabou, 2006
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Semantic Web Killer Application

• Integration?
• Semantic Web Services?
• Ontologies and P2P ?
• RDF-based Search Engine ?
• Organizational Knowledge Sharing ?
• The Semantic Web itself ?
• Not at all ?
• Anything else?

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Classics Semantic Web Applications Business
Categories

• Knowledge Management
• Enterprise Application Integration
• E-Commerce

By D. Fensel et al
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Technology Roadmap for Applications
Semantic Communication
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Industrial Ontologies Group
Semantic Search
Semantic Games
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Semantic Annotation
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Semantic Integration
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Semantic Proactivity
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Semantic Personalization
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P2P
Web Services
Agent Technology
Machine Learning
Ubiquitous Computing
Semantic Web (SW)
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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 agents / applications / software components
Smart machines, devices, homes, etc.
Web access devices
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Ontologies as Smart Resources
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Web as such is not feasible to be semantic!
NSF GENI Initiative towards Future Internet
This means that the amount of resources in the
Web will grow dramatically and without their
ontological classification and (semi- or
fully-automated) semantic annotation the
automatic discovery will be impossible.
http//www.nsf.gov/cise/geni/
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Shifting Semantic Web roadmap to the World of
Things domain
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Conclusion

• Semantic Web is about to reach its full
  potential and it would be too costly not to
  invest to it (Ora Lassila, Nokia Research
  Center, Boston, IASW-2005, Jyvaskyla)
• Semantic Web challenges still require a lot of
  work on technology and tools to facilitate
  reliable applications
• We believe that Proactive Semantic Web of Things
  can be future killer application for the
  Semantic Web
• Future Tekes policy towards Semantic Web should
  be based on two principles
• A specific program is needed (e.g. Fenix) where
  one of necessary conditions to apply should be
  developing Semantic Web methodology, technology
  and tools which is opposite to the policy of
  simply applying existing Semantic Web technology
  and tools to a particular application domain
• Consider application of existing Semantic Web
  tools and technology within other Tekes programs
  as additional advantage of project application,
  especially in domains where this technology
  essentially facilitates the progress (e.g.
  industrial automation, EAI, internet and
  networking, Ubiquitous computing, etc.).