1. Industrial Ontologies Group University of Jyväskylä SmartResource Project: (industrial case for Semantic Web and Agent Technologies) “Device”“Expert”“Service” Resource Agent http://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htm

2. Industrial Ontologies Group http://www.cs.jyu.fi/ai/OntoGroup/  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 G ROUP P ROFILE : 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.

3. IOG cooperates with different units of Jyvaskyla University and performs the activities in the domain “Industrial Applications of Semantic Web” in Finland MIT Department TITU Agora Center Adaptive Services Grid Integrated Project supported by the European Commission  Anton Naumenko  Sergiy Nikitin Proactive Self-Maintained Resources in Semantic Web SmartResource: TEKES TEKES project: ”Industrial Applications of Semantic Web” ” Industrial Applications of Semantic Web” Annual International IFIP Conference on PhD theses  Andriy Zharko  Oleksiy Khriyenko  Anton Naumenko  Sergiy Nikitin Courses:  Semantic Web and Web Services  Agent Technologies in Mobile Environment InBCT InBCT project: Semantic Search Facilitator ”Semantic Google”  " IdeaMentoring: Refining research ideas to the new business opportunities" Nokia Nokia projects:  " IdeaMentoring II "

4. GUN Concept GUN – Global Understanding eNvironment

5. 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/ai/OntoGroup/SmartResource_details.htmhttp://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htm

6. On-line learning Smart Maintenance Environment “Devices with on-line data” “Experts” Maintenance“Services”exchange data Maintenance data exchange

7. SmartResourceSmartResource SmartResourceSmartResource = GUN restricted by Maintenance Domain; Interoperability (1 st year):  Maintenance ontology;  R SC DF for dynamic and context-sensitive resource metadata;  Semantic Adapters for heterogeneous resources; Automation (2 nd year):  Agent platform for a resource;  R GB DF for ontological modeling of a resource proactive behavior in a business process;  R GB DF engine for an agent to run simple (individual) business process; Integration (3 rd year):  Multiagent platform for business process integration;  R PI DF for ontological modeling of complex business processes;  R PI DF Engine for business process integration;  Industrial Cases: ABB, Metso Automation.

8. Dimensions of RDF Development in SmartResource

9. Roles of a Resource and RDF Support

10. On-line learning Future of Smart Maintenance Environment “Devices with on-line data” “Experts”Maintenance exchange data Maintenance data exchange “Services” “ ” “ Human / patient with embedded medical sensors ” “ Doctor/ Expert ” “Medical Web Services” “Web Services for environmental diagnostics and prediction ” “Experts in environmental monitoring ” “ ” “ Environment with sensors ” “ Staff/students ” “ Staff/students with monitored organizational data ” “Web Services in organizational diagnostics and management ” “ Manager / Expert ” Objects under observation “Experts” “Services: image and video processing”

11. Obtain More Information about SmartResource from: Head of SmartResource Industrial Consortium (Steering Committee Head) Dr. Jouni Pyötsiä, Metso Automation Oy. Jouni.Pyotsia@metso.comJouni.Pyotsia@metso.com, Tel.: 040-548-3544 SmartResource Contact Person Prof. Timo Tiihonen, Vice-Rector, University of Jyväskylä tiihonen@it.jyu.fitiihonen@it.jyu.fi, Tel.: 014-260-2741 SmartResource Project Leader Prof. Vagan Terziyan, Agora Center, University of Jyväskylä vagan@it.jyu.fivagan@it.jyu.fi, Tel.: 014-260-4618

12. Semantic Web: Future Research Directions Vagan Terziyan Industrial Ontologies Group Galway, DERI, 28 April 2006

13. 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 Four Years Ago: “Six Challenges for the Semantic Web” by Richard Benjamins, Jesus Contreras, Oscar Corcho, Asuncion Gomez-Perez How well do we proceed ?

14. 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

15. 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?

16. Classics: Semantic Web Applications: Business Categories Knowledge Management Enterprise Application Integration E-Commerce By D. Fensel et al

17. Technology Roadmap for Applications Semantic Web (SW) P2PWeb ServicesAgent Technology Semantic Integration Semantic Search Semantic Proactivity Semantic Games Semantic Personalization Machine Learning Semantic Communication Semantic Annotation 1 2 3 4 5 6 7 Ubiquitous Computing Industrial Ontologies Group

18. Shared ontology Web users (profiles, preferences) Web access devices Web agents / applications / software components External world resources Smart machines, devices, homes, etc. Technological and business processes Semantic Web: which resources to annotate ? Multimedia resources Web resources / services / DBs / etc. This is just a small part of Semantic Web concern !!! Semantic annotation

19. Ontologies as Smart Resources

20. Web as such is not feasible to be semantic! NSF: GENI Initiative towards Future Internet http://www.nsf.gov/cise/geni/ 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.

21. Shifting Semantic Web roadmap to the World of Things domain

22. ConclusionConclusion “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.).