Semantic Web: The Future Starts Today

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

2. “Industrial Ontologies” Group: Our History • 1978-1984 – We took part in development of the first in USSR Industrial Natural Language Processing System “DESTA”, which included semantic analysis and ontologies; • 1985-1989 - 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 • 1990-1993 – 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. • 2001-2003 – 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 ? • more then 3,000,000,000 web-pages • “Information” burst • ICT needs comprehensive resource management technology WWW • Needs for integration of businesses • Web Services for e-Business • Standardization and Interoperability problems Business Knowledge Management • 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 Agentcities is a global, collaborative effort to construct an open network of on-line systems hosting diverse agent based services. 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 Agentcities Grid Computing 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. FIPA FIPA is a non-profit organisation aimed at producing standards for the interoperation of heterogeneous software agents. Web 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

9. Semantic Web: New “Users” applications agents

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

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

12. Ontologies: the foundation of Semantic Web 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 comment __Thing__ Author public private is-a Access Rights Location Document Related to name Report is-a is-a Web-page uri Subject Instance-of Instance-of O. Kononenko V. Terziyan public Author Author Access rights #doc1 #doc2 name Related to Semantic Web Location uri Subject comment \\AgServ\vagan\InBCT_1.doc http://www.ontogroup.net comment 3.1: analysis Home page draft 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?

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

14. Co-operative Work in Web WWW

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

16. Semantic Web is not Only ...

17. … but Also ...

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

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

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

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

25. Mobile Location-Based Service in Semantic Web

26. Machine-to-Machine Communication P2P ontology 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 P2P ontology

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

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

31. Embedded Maintenance Platforms 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 Host Agent Maintenance Service Service Agents

32. OntoServ.Net Challenges • New group of Web service users – smart industrial devices. • Internal (embedded) and external (Web-based) agent enabled service platforms. • “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-Peer concept for service network management assumes ontology-based decentralized service network management.

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

34. Semantically annotated personal data Virtually all resources have to be 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 with user information and preferences • Possibility to share information with applications at other devices and elsewhere Common data semantic descriptions(ontologies) My data descriptionmodel (ontology) mapping between views Semantic Web Inside™ Personaldata-view Commitmentto ontology Applications User data becomes available to variety of applications and other people My resourcesand their descriptions Other people’sdata-views

35. Modelling of personal data views Simple user data view (as is in most of mobile phones) 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 languagesit will be stored in universal data format. • 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…..

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

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

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

39. Using generated interface For described data model forms are generated • 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!

40. Terziyan’s Contact data Access your data quickly and easily… Possibilities to build flexible, easily customizable data management applications are great. Event data 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 Easy-to-use, flexible, customizable data management for users 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 Repositories of readydata-views Enabled collaboration and interoperability Note: Protégé-2000 ontology development and knowledge acquisition tool was used for demonstration

42. Semantic Web Ubiquitous OntoCache General ontology Translation 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 Context and preferences-based adaptation Support for semi-natural queries Effective filtering of wide variety of Web-resources Technology that supports future Ubiquitous Semantic Web

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

45. Agent-to-Agent communication semantics enables intelligent data processing Business ontological relations define possible Cooking cooperation between domain agents shared ontology Health ensures interoperability Whatever ? annotate problem domains into related ontologies programm software basing on the ontologies semantically enrich data basing on ontologies

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

47. Personal ontology General ontology PUP CGP one LIFE - many ROLES OntoGames:New Games Generation Personal User Profile Common Games Profile Personal Reality into each game

48. Personal ontology General ontology OntoGames: Semantic Games Space Semantical Games Space one LIFE - one GAME Real Life - part of the game one game - many roles

49. Personal ontology General ontology Non Stop Game - Non Stop Life OntoGames CONNECTING PEOPLE OntoGames: Exit in the Real Life Game - exit in the Real Life Reality connection via the game Reality connection via the game

50. 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. Annotated data (RDF) Ontology of gathered data Web forms and dialogs generated Processing of data by some other semantic-aware applications