1. Semantic Web: The Future Starts Today “Industrial Ontologies” Group http://www.cs.jyu.fi/ai/OntoGroup/index.html Agora Center, University of Jyväskylä, 23 May 2003 Industrial Ontologies Group

2. “Industrial Ontologies” Group: Our History ontologies1978-1984 – We took part in development of the first in USSR Industrial Natural Language Processing System “DESTA”, which included semantic analysis and ontologies; Enabled Semantic AnnotationDiscovery Integration Semantic Web Services1985-1989 - We took part in development of the first in USSR Industrial Automated Natural Language Programming System “ALISA”, which Enabled Semantic Annotation, Discovery and Integration of software components (prototype of today's Semantic Web Services concept);

3. “Industrial Ontologies” Group: Our History Semantic Web1990-1993 – under name of Metaintelligence Lab. we were piloting concept of a Metasemantic Network (triplet-based (meta-)knowledge representation model) – prototype of today’s RDF- based knowledge representation in Semantic Web; 1994-2000 – various projects with industrial partners, e.g. MetaAtom – “Semantic Diagnostics of Ukrainian Nuclear Power Stations based on Metaknowledge”; MetaHuman – industrial medical diagnostics expert system based on Metaknowledge”; Jeweler – metamodelling and control of industrial processes, etc.; got several research grants from Finnish Academy;

4. “Industrial Ontologies” Group: Our History 2000-2001 – we have created branches in Vrije Universiteit Amsterdam (heart of Semantic Web activities in Europe) where now working 5 our former team members, in Jyvaskyla University (several tens of researchers) and established research groups in Kharkov (Ukraine) on Data Mining, Educational Ontologies, Telemedicine, etc. Semantic Web2001-2003 – we took part in MultiMeetMobile Tekes Project, in InBCT Tekes Project in Tempus EU Compact Project in (or in cooperation with) University of Jyvaskyla where we further promote Semantic Web concepts.

5. Industrial Ontologies Group: Important Objective For us there are no doubts about the possibilities, which Semantic Web opens for industry. that is why one important objective of our activities is to study appropriate industrial cases, collect arguments, launch industrial projects and develop prototypes for the industrial companies to not only believe together with us but also benefit from the Semantic Web.

6. Why and Where Semantic Web ? WWW Business Knowledge Management  more then 3,000,000,000 web-pages  “Information” burst  ICT needs comprehensive resource management technology  Needs for integration of businesses  Web Services for e-Business  Standardization and Interoperability problems  Consolidate and reuse experience  Standardize knowledge sharing technology  Needs for the intelligent tools to use human’s knowledge

7. Approach: 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.

8. Word-Wide Correlated Activities Semantic Web Grid Computing Web Services Agentcities Agentcities is a global, collaborative effort to construct an open network of on-line systems hosting diverse agent based services. WWW is more and more used for application to application communication. The programmatic interfaces made available are referred to as Web services. The goal of the Web Services Activity is to develop a set of technologies in order to bring Web services to their full potential FIPA FIPA is a non-profit organisation aimed at producing standards for the interoperation of heterogeneous software agents. Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation Wide-area distributed computing, or "grid” technologies, provide the foundation to a number of large-scale efforts utilizing the global Internet to build distributed computing and communications infrastructures.

9. Semantic Web: New “Users” applications agents

10. Semantic Web: Resource Integration Shared ontology Web resources / services / DBs / etc. Semantic annotation

11. Semantic Web: What to Annotate ? Web resources / services / DBs / etc. Shared ontology Web users (profiles, preferences) Web access devices Web agents / applications External world resources Smart machines and devices

12. Ontologies: the foundation of Semantic Web Document Location Subject name is-a uri comment__Thing__ is-a Report Web-page Access Rights Author http://www.ontogroup.net is-a \\AgServ\vagan\InBCT_1.doc V. Terziyan Author O. Kononenko Author uriLocation draft comment public Home page comment 3.1: analysis Subject Instance-of Query 1: get all documents from location X, but not web-pages Query 2: get documents related to Y, with more then one author, one of which is Terziyan Query 3: are there web-pages of Z with “private” access related to documents with subject S? Related to Access rights #doc1 #doc2 Ontologies are key enabling technology for the Semantic Web “..explicit specification of conceptualization..” Ontology is formal and rich way to provide shared and common understanding of a domain, that can be used by people and machines Semantic Web name public private

13. Semantic Web: Interoperability Ontology A: DocumentsOntology B: Research A commitment to a common ontology is a guarantee of a consistency and thus possibility of data (and knowledge) sharing Common (shared) ontology Ontology C: Services System 1 System 2 \\AgServ\vagan\InBCT_1.doc V. Terziyan A:Report A:Location 3.1: analysis A:Subject A:Author Instance-of Semantic Web A:name

14. Co-operative Work in Web WWW

15. Co-operative Work in Semantic Web WWW Semantic Web

16. Semantic Web is not Only...

17. but Also … but Also...

18. Industrial Ontologies Group Samples of our Research: “Applications of Semantic Web”

19. Web Resource/Service Integration: Server-Based Transaction Monitor ServerClient Server Web resource / service Web resource / service Transaction Service TM wireless

20. Web Resource/Service Integration: Mobile Client-Base Transaction Monitor Server Client Server Web resource / service TM Web resource / service wireless

21. The conceptual scheme of the ontology-based transaction management with multiple e-services Terziyan V., Ontological Modelling of E-Services to Ensure Appropriate Mobile Transactions, In: International Journal of Intelligent Systems in Accounting, Finance and Management, J. Wiley & Sons, Vol. 12, 2003, 14 pp.

22. Ontology-Based Transaction Management for the Semantic Web Consider two basic transaction management architectures in mobile environment depending on where the Transaction Monitor (TM) will be located. First one (Server-Based) assumes that TM will be located in server side, e.g. within some transaction management service. Second one (Client- Based) supposes that TM is located in mobile client terminal. The first objective will be to provide and study an integrated mobile transaction management architecture for the Semantic Web applications, which will combine the best features from these two architectures by intelligent switching from one architecture to another one depending on current application context. There is already some ontological support for Semantic Web resources and services interoperability based on OWL, DAML-S. However to be able to manage transactions in Semantic Web across multiple resources (or services) there will not be enough only ontologies for semantic annotations of these resources; there will be evident need of the ontology for the Semantic Web transactions itself. The second objective will be developing pilot ontology for the RDF-based semantic annotation of mobile transactions in the Semantic Web.

23. Architecture for a Mobile P-Commerce Service Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.

24. BANK: P-Commerce Service provider Personal ontology General ontology Automatic: Mapping and Transactions Service User via resources and users annotations

25. Mobile Location-Based Service in Semantic Web

26. Machine-to-Machine Communication P2P ontology Heterogeneous machines can “understand” each other while exchanging data due to shared ontologies

27. Semantic Web-Supported Sharing and Integration of Web Services Different companies would be able to share and use cooperatively their Web resources and services due to standardized descriptions of their resources. P2P ontology

28. Corporate/Business Hub Publish own resource descriptions Advertise own services Lookup for resources with semantic search Automated access to enterprise (or partners’) resources Hub ontology and shared domain ontologies Seamless integration of services Software and data reuse Partners / Businesses What parties can do: What parties achieve: Ontologies will help to glue such Enterprise-wide / Cooperative Semantic Web of shared resources Companies would be able to create “Corporate Hubs”, which would be an excellent cooperative business environment for their applications.

29. Web Services for Smart Devices Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc. OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,

30. Global Network of Maintenance Services OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,

31. Embedded Maintenance Platforms Service Agents Host Agent Embedded Platform Based on the online diagnostics, a service agent, selected for the specific emergency situation, moves to the embedded platform to help the host agent to manage it and to carry out the predictive maintenance activities Maintenance Service

32. OntoServ.Net Challenges smart industrial devicesNew group of Web service users – smart industrial devices. Internalexternal service platformsInternal (embedded) and external (Web-based) agent enabled service platforms. Mobile Service Component“Mobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side. Semantic Peer-to-PeerSemantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.

33. Industrial Ontologies Group Future Plans: Applications of Wireless Semantic Web Industrial Ontologies Group Future Plans: “Applications of Wireless Semantic Web”

34. Semantically annotated personal data Virtually all resources have to be a marked with semantic labels that show explicitly the meaning of the resource (piece of data, fact, value etc.) It will make possible for user: –To organize own view on data and use it for data management –To access own and other’s resources with semantic queries using “terms” of own model –To be able integrate data from other sources (semantics of data is important, data can be converted/translated if needed and appropriate mapping exists) Applications will have: –Possibility to discover and operate on user information and preferences –Possibility to share information with applications on other devices and elsewhere My data description model (ontology) Common data semantic descriptions (ontologies) My resources and their descriptions Personal data-view Applications mapping between views Other people’s data-views User data becomes available to variety of applications and other people Semantic Web Inside™ Commitment to ontology

35. Modelling of personal data views Simple user data view (as is in most of mobile phones) Model of user’s data and other resources: - Contacts (phone numbers, names etc.) - Notes (some pieces of text) - Calendar (with some events assigned) It is rather simple, but a good beginning for own data model creation….. Data to store in every instance of defined information model Actually, this model is a simple ontology of “Personal Data” domain. Using developed standard ontology languages it will be stored in universal data format.

36. Building own data model… added slot (property/field) inherited slot

37. Building own data structure added slot (property/field) inherited slot Inherited properties “Relative is a kind of friend” Links to other data entities

38. Building own data structure added slot (property/field) inherited slot Customized data model: new kinds of data new kinds of data new kinds of representation new kinds of representation rules and constraints for data etc. rules and constraints for data etc. association of data with applications association of data with applications Customized data model: new kinds of data new kinds of data new kinds of representation new kinds of representation rules and constraints for data etc. rules and constraints for data etc. association of data with applications association of data with applications

39. Using generated interface Data view is described as an ontology which contains all needed information about data structure. User interface is built dynamically from ontology: Fields for data Form layout, types of controls (e.g. picture, checkboxes etc.) Rules for data that can check some constraints, invoke actions, perform calculations – whatever! For described data model forms are generated

40. Access your data quickly and easily… Terziyan’s Contact data Event data Possibilities to build flexible, easily customizable data management applications are great. Just click to open Every piece of data is somehow described in user’s terms from data-view ontology. Links between data make it easy to find needed information

41. Customizable personal information management environment Personal data “view”: Development of own view on personal data Reusing of existing views (join, modify, extend) Links between personal and some “global” ontology Sharing of data: Applications use data and do it correctly (because of semantics assigned) Applications can exchange data with external sources Data can be translated in respect of its semantics (for localization, between different data views, to fit some requirements etc.) In such environment even development of own applications/scripts can be possible Ontologies and Semantic Web will enable such kind of applications Easy-to-use, flexible, customizable data management for users Repositories of ready data-views Note: Protégé-2000 ontology development and knowledge acquisition tool was used for demonstration Enabled collaboration and interoperability

42. OntoCache General ontology Semantic annotations of Web-services (or any other resources) based on shared ontologies enhance much the efficiency of their search/browsing from the PDA. Local ontology adapts permanently to the user preferences. Personal ontology

43. OntoCache: benefits Technology that supports future Ubiquitous Semantic Web Effective filtering of wide variety of Web-resources Support for semi-natural queries Context and preferences- based adaptation

44. Phone calls are also possible between mobile terminal agents. They are performed without human participation in order to exchange local information. Agent-to-Agent communication Semantic annotation of the local data enables its intelligent processing by software. Ontologies provide interoperability between heterogeneous peers.

45. Agent-to-Agent communication Health Cooking Business ? Whatever semantics enables intelligent data processing ontological relations define possible cooperation between domain agents shared ontology ensures interoperability

46. Telemedicine Intheoffice In the office Outside Fishing Anywhere At university On a beach Health Center Health Maintenance without barriers Anytime and Anywhere

47. Telemedicine Intheoffice In the office Outside Fishing Anywhere At university On a beach Health Center Cases of “Human Maintenance” Activities Interaction “Recovery” Agents “Diagnostic” Agents “Platform Steward” “WatchDog” “Therapist” Human and Local Health Maintenance Center Remote Health Maintenance Center “Recovery” Agents “Diagnostic” Agents “Therapist” “Platform Steward” Maintenance Crew Service Health Maintenance without barriers Anytime and Anywhere

48. CGP PUP Personal User Profile Common Games Profile Personal ontology General ontology OntoGames : New Games Generation

49. CGP PUP Personal User Profile Common Games Profile Personal ontology General ontology

50. Personal ontology General ontology OntoGames : Semantical Games Space

51. Personal ontology General ontology

52. OntoGames : Exit in the Real Life Reality connection via the game Reality connection via the game General ontology Personal ontology Non Stop Game - Non Stop Life OntoGames C ONNECTING P EOPLE

53. BANK : Data annotation In order to make miscellaneous data gathered and used later for some processing, every piece of data needs label assigned, which will denote its semantics in terms of some ontology. Software that is developed with support of that ontology can recognize the data and process it correctly in respect to its semantics. Ontology of gathered data Web forms and dialogs generated Annotated data (RDF) Processing of data by some other semantic-aware applications

54. BANK : Customer’s data processing Data Storage Bank Clients Ontology Bank Clients Input forms Intelligent ontology-based software Clients clustering

55. BANK : Services annotation Semantics enabled services – easy way to use for customer Semantically annotated bank services I want to … Information filing, all documentation and transactions Less detailed information Agent-assistant Customer

56. BANK : Loan Borrower annotation Loan borrowers Bank - investor Automated support of: making decisions about trusting making decisions about trusting prediction of future trends prediction of future trends via semantically annotated loan borrowers information via semantically annotated loan borrowers information

57. Read Our Recent Reports Semantic Web: The Future Starts TodaySemantic Web: The Future Starts Today –(collection of research papers and presentations of Industrial Ontologies Group for the Period November 2002-April 2003) Semantic Web and Peer-to-Peer: Integration and Interoperability in IndustrySemantic Web and Peer-to-Peer: Integration and Interoperability in Industry Semantic Web Enabled Web Services: State-of-Art and ChallengesSemantic Web Enabled Web Services: State-of-Art and Challenges Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance SystemDistributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance System Available online in: http://www.cs.jyu.fi/ai/OntoGroup/index.htmlhttp://www.cs.jyu.fi/ai/OntoGroup/index.html Industrial Ontologies Group V. Terziyan A. Zharko O. Kononenko O. Khriyenko

58. Semantic Web: The Future starts today e-Business, net-markets e-Business, net-markets “Web Of Trust” E nterprise A pplication I ntegration E nterprise A pplication I ntegration Interoperability standards Web-services

59. Industrial Ontologies Group: Examples of Related Contacts

60. University of Jyvaskyla Experience: Examples of Related Courses

61. Cooperation with American Universities Ioannis Kakadiaris University of Houston Ioannis Kakadiaris Ass. Professor, Department of Computer Science, University of Houston, USA Ioannis is the founder and Director of Visual Computing Laboratory and the Director of the Division of Bioimaging and Biocomputation at the UH Institute for Digital Informatics and Analysis. He is the recipient of a year 2000 NSF Early Career Development Award. Cooperation focuses to investigating issues related to management of the Web content which includes human motions as its component, according to the common framework of management multimedia content in the Semantic Web. Possible applications considered: - Automatic remote camera control (behavior recognition, intentions capture, operator (astronaut) actions control etc.) - Semantic video transmission (transmit wireless only recognized semantics of motions). John Canny Professor, University of California, Berkeley Division of Computer Science, University of California, Berkeley, USA John came from MIT in 1987 after his thesis on robot motion planning, which won the ACM dissertation award. He received a Packard Foundation Fellowship and a PYI while at Berkeley. He developed inexpensive, ubiquitous telepresence robots called "PRoPs”... Cooperation focuses to following subjects: - Knowledge management of a community of trust; - Collaborative Filtering with Privacy; - Intelligent Integration of Filtering Models; - Adaptive User Interfaces; - Human-Centered Computing; - Online Collaborative learning.

62. Conclusion technologySemantic Web is not only a technology as many used to name it; environmentSemantic Web is not only an environment as many naming it now; Semantic WebcontextSemantic Web it is a new context within which one should rethink and re-interpret his existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance… ------------------------------------------ Contact: Vagan Terziyan vagan@it.jyu.fivagan@it.jyu.fi http://www.cs.jyu.fi/ai/vaganhttp://www.cs.jyu.fi/ai/vagan (tel. +358 14 2604618)