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Industrial Ontologies Group
University of JyväskyläUniversity of Jyväskylä
SmartResource Project:SmartResource Project: (industrial case for Semantic Web and Agent Technologies)(industrial case for Semantic Web and Agent Technologies)
““Device”Device”
““Expert”Expert”
““Service”Service”
Resource Resource AgentAgent
Resource Resource AgentAgent
Resource Resource AgentAgent
http://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htm
Industrial Ontologies GroupIndustrial Ontologies GroupIndustrial Ontologies GroupIndustrial Ontologies Group
Industrial Ontologies Grouphttp://www.cs.jyu.fi/ai/OntoGroup/
Semantic WebSemantic Web and Ontologies and Ontologies
Web Services and Semantic Web ServicesWeb Services and Semantic Web Services
(Multi) Agent Technologies(Multi) Agent Technologies
Distributed Artificial IntelligenceDistributed Artificial Intelligence
Knowledge ManagementKnowledge Management
Ubiquitous ComputingUbiquitous Computing
Mobile Context-Aware Services and ApplicationsMobile Context-Aware Services and Applications
Machine Learning, Data Mining and Knowledge DiscoveryMachine Learning, Data Mining and Knowledge Discovery
GGROUP ROUP PPROFILEROFILE::
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.
IOG cooperates with different units of Jyvaskyla IOG cooperates with different units of Jyvaskyla University and performs the activities in the domain University and performs the activities in the domain
“Industrial Applications of Semantic Web” in Finland“Industrial Applications of Semantic Web” in Finland
IOG cooperates with different units of Jyvaskyla IOG cooperates with different units of Jyvaskyla University and performs the activities in the domain University and performs the activities in the domain
“Industrial Applications of Semantic Web” in Finland“Industrial Applications of Semantic Web” in Finland
MITMIT Department Department
TITUTITU
Agora CenterAgora Center
Adaptive Services GridAdaptive Services GridIntegrated Project supported Integrated Project supported by the European Commissionby the European Commission
Anton NaumenkoAnton Naumenko Sergiy NikitinSergiy Nikitin
Proactive Self-Maintained Proactive Self-Maintained Resources in Semantic WebResources in Semantic Web
SmartResource:SmartResource:
TEKES TEKES project:
”” Industrial Applications of Industrial Applications of Semantic Web”Semantic Web”
Annual International IFIP Conference onAnnual International IFIP Conference on
PhD thesesPhD theses Andriy ZharkoAndriy Zharko Oleksiy KhriyenkoOleksiy Khriyenko Anton NaumenkoAnton Naumenko Sergiy NikitinSergiy Nikitin
Courses:Courses: Semantic Web and Web ServicesSemantic Web and Web Services Agent Technologies in Mobile Agent Technologies in Mobile EnvironmentEnvironment
InBCT InBCT project:Semantic Search FacilitatorSemantic Search Facilitator
””Semantic Semantic GoogleGoogle””
""IdeaMentoring:IdeaMentoring: Refining research ideas to Refining research ideas to the new business opportunities"the new business opportunities"
Nokia Nokia projects:
""IdeaMentoring IIIdeaMentoring II " "
GUN ConceptGUN ConceptGUN ConceptGUN Concept
GUN – Global Understanding eNvironment
WIDER OBJECTIVEWIDER OBJECTIVEWIDER OBJECTIVEWIDER 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.htm
On-line learning
On-line learning
Smart Maintenance EnvironmentSmart Maintenance EnvironmentSmart Maintenance EnvironmentSmart Maintenance Environment
““Devices with Devices with on-line data”on-line data”
““Experts”Experts”
Maintenance
Maintenance
““Services”Services”
exchangeexchange
datadata
Maintenance
Maintenance
datadata
exchange
exchange
SmartResourceSmartResourceSmartResourceSmartResource
• SmartResourceSmartResource = 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.
Dimensions of RDF Development in Dimensions of RDF Development in SmartResourceSmartResource
Dimensions of RDF Development in Dimensions of RDF Development in SmartResourceSmartResource
Roles of a Resource and RDF SupportRoles of a Resource and RDF SupportRoles of a Resource and RDF SupportRoles of a Resource and RDF Support
On-line learning
On-line learning
Future of Smart Maintenance EnvironmentFuture of Smart Maintenance EnvironmentFuture of Smart Maintenance EnvironmentFuture of Smart Maintenance Environment
““Devices with Devices with on-line data”on-line data”
““Experts”Experts”
Maintenance
Maintenanceexchangeexchange
datadata
Maintenance
Maintenance
datadata
exchange
exchange
““Services”Services”
““Human/patient with embedded medical sensors ””
““DoctorDoctor//ExpertExpert””
““Medical Web Medical Web Services”Services”““Web Services Web Services for environmental for environmental
diagnostics and predictiondiagnostics and prediction””
““ExpertsExperts in environmental in environmental
monitoringmonitoring””
““Environment
with sensors ””
““Staff/studentsStaff/students
with monitored organizational data””
““Web Services Web Services in in organizational diagnostics and organizational diagnostics and
managementmanagement””
““ManagerManager//ExpertExpert””
Objects under Objects under observationobservation
““Experts”Experts”
““Services: image and Services: image and video processing”video processing”
Obtain More Information about Obtain More Information about SmartResource from:SmartResource from:Obtain More Information about Obtain More Information about SmartResource from:SmartResource from:
Head of SmartResource Industrial Consortium (Steering Committee Head) Dr. Jouni Pyötsiä, Metso Automation Oy.
[email protected] , Tel.: 040-548-3544
SmartResource Contact Person Prof. Timo Tiihonen, Vice-Rector, University of Jyväskylä
[email protected] , Tel.: 014-260-2741
SmartResource Project Leader Prof. Vagan Terziyan, Agora Center, University of Jyväskylä
[email protected] , Tel.: 014-260-4618
Semantic Web: Future Research Semantic Web: Future Research DirectionsDirections
Vagan Terziyan
Industrial Ontologies Group Galway, DERI, 28 April 2006
Challenge 1: Availability of ContentChallenge 1: Availability of Content
Challenge 2: Ontology Availability, Development and EvolutionChallenge 2: Ontology Availability, Development and Evolution
Challenge 3: Scalability of Semantic Web ContentChallenge 3: Scalability of Semantic Web Content
Challenge 4: MultilingualityChallenge 4: Multilinguality
Challenge 5: VisualizationChallenge 5: Visualization
Challenge 6: Semantic Web Language StandardizationChallenge 6: Semantic Web Language Standardization
Four Years Ago: “Six Challenges for the Four Years Ago: “Six Challenges for the Semantic Web”Semantic Web”
by Richard Benjamins, Jesus Contreras, Oscar Corcho, Asuncion Gomez-Perez
How well do we proceed ?
Vision 2006: “Real Semantic Web”Vision 2006: “Real Semantic Web”Vision 2006: “Real Semantic Web”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
Semantic Web Killer ApplicationSemantic 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?
Classics: Semantic Web Classics: Semantic Web Applications: Business CategoriesApplications: Business Categories
• Knowledge Management
• Enterprise Application Integration
• E-Commerce
By D. Fensel et al
Technology Roadmap for ApplicationsTechnology Roadmap for ApplicationsTechnology Roadmap for ApplicationsTechnology Roadmap for Applications
Semantic Web (SW)
P2PWeb Services Agent 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
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 ?Semantic Web: which resources to annotate ?Semantic Web: which resources to annotate ?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
Ontologies as Smart ResourcesOntologies as Smart ResourcesOntologies as Smart ResourcesOntologies as Smart Resources
Web as such is not feasible to be Web as such is not feasible to be semanticsemantic! ! Web as such is not feasible to be Web as such is not feasible to be semanticsemantic! !
NSF: GENI Initiative NSF: GENI Initiative towards Future Internettowards Future Internet
NSF: GENI Initiative NSF: GENI Initiative towards Future Internettowards 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.
Shifting Semantic Web roadmap to Shifting Semantic Web roadmap to the World of Things domain the World of Things domain
Shifting Semantic Web roadmap to Shifting Semantic Web roadmap to the World of Things domain the World of Things domain
ConclusionConclusionConclusionConclusion
• “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.).
