1. Intelligent Web Applications (Part 1) Course Introduction Vagan Terziyan AI Department, Kharkov National University of Radioelectronics / MIT Department, University of Jyvaskyla vagan@it.jyu.fi ; terziyan@yahoo.com http://www.cs.jyu.fi/ai/vagan/index.html +358 14 260-4618 Vrije Universiteit Amsterdam, Fall 2003

2. 2 Contents §Course Introduction §Lectures and Links §Examples of course-related research

3. 3 Course (Part 1) Formula: Web Personalization + Web Mining + + Semantic Web + Intelligent Agents = = Intelligent Web Applications - Why ? - To be able to intelligently utilise huge, rich and shared web resources and services taking into account heterogeneity of sources, user preferences and mobility. - What included ? - Introduction to Web content management. Web content personalization. Filtering Web content. Data and Web mining methods. Multidatabase mining. Metamodels for knowledge management. E-services and their management in wired and wireless Internet. Intelligent e-commerce applications and mobility of users. Information integration of heterogeneous resources.

4. 4 Practical Information §6 Lectures (2 x 45 minutes each, in English) during period 24 November - 5 December according to the schedule; §Course slides: available online (links from this lecture)

5. 5 Introduction: Semantic Web - new Possibilities for Intelligent Web Applications

6. 6 Motivation for Semantic Web

7. 7 Semantic Web Content: New “Users” applications agents

8. 8 Some Professions around Semantic Web Content Agents Annotations Ontologies Software engineers Ontology engineers Web designers Content creators Logic, Proof and Trust AI Professionals Mobile Computing Professionals

9. 9 Semantic Web: Resource Integration Shared ontology Web resources / services / DBs / etc. Semantic annotation

10. 10 What else Can be Annotated for Semantic Web ? Web resources / services / DBs / etc. Shared ontology Web users (profiles, preferences) Web access devices Web agents / applications External world resources Smart machines and devices

11. 11 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.

12. 12 IWA Course (Part 1): Lectures

13. 13 Lecture 1: This Lecture – IWA (1) Introduction http://www.cs.jyu.fi/ai/vagan/IWA_1_Introduction.ppt

14. 14 Lecture 2: Web Content Personalization Overview http://www.cs.jyu.fi/ai/vagan/Personalization.ppt

15. 15 Lecture 3: Collaborative Filtering http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt

16. 16 Lecture 4: Personalization in Mobile Environment http://www.cs.jyu.fi/ai/vagan/Mobile_Personalization.ppt

17. 17 Lecture 5: Web Mining http://www.cs.jyu.fi/ai/vagan/Web_Mining.ppt

18. 18 Lecture 6: Multidatabase Mining http://www.cs.jyu.fi/ai/vagan/MDB_Mining.ppt

19. 19 University of Jyvaskyla (Industrial Ontologies Group) Experience: Examples of Course-Related Research

20. 20 Mobile Location-Based Service in Semantic Web

21. 21 Mobile Transactions Management in Semantic Web

22. 22 P-Commerce in Semantic Web 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.

23. 23 Semantic Metanetwork for Metadata Management Semantic Metanetwork is considered formally as the set of semantic networks, which are put on each other in such a way that links of every previous semantic network are in the same time nodes of the next network. In a Semantic Metanetwork every higher level controls semantic structure of the lower level. Terziyan V., Puuronen S., Reasoning with Multilevel Contexts in Semantic Metanetworks, In: P. Bonzon, M. Cavalcanti, R. Nossun (Eds.), Formal Aspects in Context, Kluwer Academic Publishers, 2000, pp. 107-126.

24. 24 Petri Metanetwork for Management Dynamics A metapetrinet is able not only to change the marking of a petrinet but also to reconfigure dynamically its structure Each level of the new structure is an ordinary petrinet of some traditional type. A basic level petrinet simulates the process of some application. The second level, i.e. the metapetrinet, is used to simulate and help controlling the configuration change at the basic level. Terziyan V., Savolainen V., Metapetrinets for Controlling Complex and Dynamic Processes, International Journal of Information and Management Sciences, V. 10, No. 1, March 1999, pp.13-32.

25. 25 Bayesian Metanetwork for Management Uncertainty Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content Personalization, In: Proceedings of 2nd WSEAS International Conference on Automation and Integration (ICAI’02), Puerto De La Cruz, Tenerife, December 2002.

26. 26 Multidatabase Mining based on Metadata Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.), Database and Expert Systems Applications, Lecture Notes in Computer Science, Springer-Verlag, V. 1677, 1999, pp. 882-891.

27. 27 Machine-to-Machine Communication P2P ontology Heterogeneous machines can “understand” each other while exchanging data due to shared ontologies

28. 28 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

29. 29 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.

30. 30 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,

31. 31 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,

32. 32 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

33. 33 OntoServ.Net Challenges smart industrial devices §New group of Web service users – smart industrial devices. §Internalexternal service platforms §Internal (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-Peer §Semantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.

34. 34 Agents in Semantic Web 1. “I feel bad, pressure more than 200, headache, … Who can advise what to do ? “ 4. “Never had such experience. No idea what to do” 3. “Wait a bit, I will give you some pills” 2. “ I think you should stop drink beer for a while “ Agents in Semantic Web supposed to understand each other because they will share common standard, platform, ontology and language

35. 35 The Challenge: Global Understanding eNvironment (GUN) How to make entities from our physical world to understand each other when necessary ?.. … Its elementary ! But not easy !! Just to make agents from them !!!

36. 36 GUN Concept Entities will interoperate through OntoShells, which are “supplements” of these entities up to Semantic Web enabled agents 1. “I feel bad, temperature 40, pain in stomach, … Who can advise what to do ? “ 2. “I have some pills for you”

37. 37 Semantic Web: Before GUN Semantic Web Resources Semantic Web Applications Semantic Web applications “understand”, (re)use, share, integrate, etc. Semantic Web resources

38. 38 GUN Concept: All GUN resources “understand” each other Real World objects OntoAdapters Real World Object + + OntoAdapter + + OntoShell = GUN Resource = GUN Resource GUN OntoShells Real World objects of new generation (OntoAdapter inside)

39. 39 Interoperability of Heterogeneous Software Recently in increasing frequency a problem of interaction between heterogeneous software rises. Semantic annotation of exchange data based on common ontology will enable interoperability and intelligent processes support. (Semantic)GUN environment Java package Dynamic Link Library Database server cgi-script (semantic) OntoAdapter

40. 40 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: l To organize own view on data and use it for data management l To access own and other’s resources with semantic queries using “terms” of own model l 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: l Possibility to discover and operate with user information and preferences l Possibility to share information with applications at 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

41. 41 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.

42. 42 Building own data model… added slot (property/field) inherited slot

43. 43 Building own data structure added slot (property/field) inherited slot Inherited properties “Relative is a kind of friend” Links to other data entities

44. 44 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

45. 45 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

46. 46 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

47. 47 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

48. 48OntoCache 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

49. 49 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

50. 50 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.

51. 51 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

52. 52 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

53. 53 OntoGames : New Games Generation CGP PUP Personal User Profile Common Games Profile Personal ontology General ontology

54. 54 OntoGames : Semantic Games Space Personal ontology General ontology

55. 55 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

56. 56 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

57. 57 BANK : Customer’s data processing Data Storage Bank Clients Ontology Bank Clients Input forms Intelligent ontology-based software Clients clustering

58. 58 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

59. 59 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

60. 60 Read Our Recent Reports §Semantic Web: The Future Starts Today l (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 Industry §Semantic Web Enabled Web Services: State-of-Art and Challenges §Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance System §Available online in: http://www.cs.jyu.fi/ai/OntoGroup Industrial Ontologies Group V. Terziyan A. Zharko O. Kononenko O. Khriyenko