1. Semantic Web: State-of-Art and Opportunities “Industrial Ontologies” Group http://www.cs.jyu.fi/ai/OntoGroup/index.html University of Jyväskylä, August 2003 Industrial Ontologies Group

2. Our Team: “Industrial Ontologies” Group Head: –Vagan Terziyan Researchers: –Oleksandr Kononenko –Andriy Zharko –Oleksiy Khriyenko –Olena Kaykova –… Consultant (Metso Oy): –Jouni Pyotsia Manager ( Science Park ): –Mikko Kovalainen vagan@it.jyu.fi MIT Department, University of Jyväskylä “Industrial Ontologies” Group: http://www.cs.jyu.fi/ai/OntoGroup/index.html

3. “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);

4. “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;

5. “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.

6. 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.

7. 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

8. Motivation for Semantic Web

9. What is the “Transactional Web” Today: “The eye-ball Web” - the architecture of the Web is geared towards delivering information visually. Tomorrow: “The transactional Web” – the architecture of the Web geared towards intelligently exchanging information between applications.

10. Summarizing the Problem: Computers don’t understand Meaning “My mouse is broken. I need a new one…”

11. An Example Use of ontology “My mouse is broken” vs. “My mouse is dead”

12. 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.

20. Word-Wide Correlated Activities Semantic Web Grid Computing Web Services Agentcities Global, collaborative effort to construct an open network of on-line systems hosting diverse agent based services. Providing technologies for automated communication, discovery and integration of Web services, to enable on-the-fly software composition through the use of loosely coupled, reusable software components. FIPA Producing standards for the interoperation of heterogeneous software agents. Extending current web by giving information a given well-defined meaning, better enabling computers and people to work in cooperation Utilizing the global Internet to build distributed computing and communications infrastructures.

21. HTML 100% 50% 0% XML DAML+OIL 2000 2005 2010 “Fifty percent of the content on the Web will be in XML format by the end of 2003” ……….Gartner Group “In 30 years e-commerce will have become second nature. Lifelike, intelligent virtual assistants will be performing most routine transactions and simple negotiations electronically on our behalf. More technological change will have taken place in that period than during the entire twentieth century, and the curve will continue to steepen exponentially into the foreseeable future.” Ray Kurzweil Web Migration to New Technology

22. Tim Berners-Lee's Vision of Semantic Web (IJCAI-01)

23. Semantic Web: New “Users” applications agents

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

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

26. 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

27. The Semantic Web

28. Can’t we just use XML? This is what a web-page in natural language looks like for a machine J. Hendler

29. XML helps CV name education work private XML allows “meaningful tags” to be added to parts of the text

30. J. Hendler XML  machine accessible meaning CV name education work private But to your machine, the tags look like this….

31. J. Hendler Schemas take a step in the right direction Schemas help…. …by relating common terms between documents 

32. But other people use other schemas CV name education work private   >  Someone else has one like this…. J. Hendler

33. The “semantics” isn’t there …which don’t fit in  J. Hendler

34. KR provides “external” referents to merge on Semantic Web languages add mappings and structure.        J. Hendler

35. Semantic Web basics…  RDF: is a W3C standard, which provides tool to describe Web resources provides interoperability between applications that exchange machine-understandable information  RDF Schema: – is a W3C standard which defines vocabulary for RDF – organizes this vocabulary in a typed hierarchy – capable to explicitly declare semantic relations between vocabulary terms

36. Ontological Vision of Semantic Web Semantic Web needs ontologies An ontology is  document or file that formally and in a standardized way defines the hierarchy of classes within the domain, semantic relations among terms and inference rules Use of ontologies:  Sharing semantics of your data across distributed applications

37. 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

38. 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

39. Co-operative Work in Web WWW

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

41. Semantic Web is not Only...

42. but Also … but Also...

43. Enterprise Integration Technologies Web Service Technology (SOAP, WSDL and UDDI); Enterprise Integration (Enterprise Application Integration and E-Commerce in form of Business-to-Business Integration as well as Business-to-Consumer); Semantic Web Technology (ontology languages). The promise is that Web Service Technology in conjunction with Semantic Web Technology (“Semantic Web Services”) will make Enterprise Integration dynamically possible for all types and sizes of enterprises compared to the “traditional” technologies

44. The Web Services Stack Wire ProtocolDescriptionDiscovery SOAPWSDLRegistry (UDDI) provides a standard, flexible communications channel provides a standard, flexible way to describe what and how a Web service does what it does provides a standard, flexible way to discover where a Web service is located and where to find more information about what the Web service does interoperability at the lowest level interoperability at the content level dynamic discovery

45. Six Challenges for the Semantic Web Richard Benjamins, Jesus Contreras, Oscar Corcho, Asuncion Gomez-Perez April 2002

46. Semantic Web content is a content annotated according to particular ontologies, which define the meaning of the words or concepts appearing in the content. Currently, there is little Semantic Web content available. Researchers are building tools to support semantic annotation. However, they have two limitations: 1.Most of them annotate only static pages, and 2.Many of them focus on creating new content. There is a need need to create a set of annotation services (middleware) concerning static and dynamic web documents, which may include multimedia, and web services. Challenge 1: Availability of Content

47. Constructing of kernel ontologies to be used by all the domains. E.g. IEEE Standard Upper Ontology Group aims to create a common unified top level ontology, also RosettaNet, etc. Providing methodological and technological support for most of the activities of the ontology development process. Managing evolution of ontologies and their relation to already annotated data. Configuration management tools are necessary to keep control of the versions of each ontology as well as the interdependencies between them and annotations. Challenge 2: Ontology Availability, Development and Evolution

48. Once we have the Semantic Web content, we need to worry about how to manage it in a scalable manner, that is, how to organize it, where to store it and how to find the right content: Storage and organization of Semantic Web pages. The ‘basic’ Semantic Web consists of ontology-based annotated pages whose linking structure reflects the structure of the WWW, that is, pages connected to others by means of hyperlinks. This hyperlinked configuration does not fully exploit the underlying semantics of Semantic Web pages. We foresee the use of semantic indexes to group Semantic Web content based on particular topics. Semantic indexes will be generated dynamically using ontological information and annotated documents. Finding of information in the Semantic Web. A mechanism of coordination among semantic indexes must be provided for the easy finding of SW content taking into account the semantics of web resources. A peer to peer architecture could be explored, similar to the current configuration of routers in the WWW. Indexes could be considered as active agents that know what topics they can handle. Topics that do not occur in the index are semantically routed to neighbour indexes. The use of agents should be explored for negotiation techniques in order to obtain the semantic routing of topics. Challenge 3: Scalability of Semantic Web Content

49. Multilinguality plays an increasing role at the level of ontologies, of annotations and of user interface: At the ontology level, ontology builders may want to use their native language for the development of the ontologies in which annotations will be based. At the annotation level, annotation of content can be performed in various languages. Since more users (especially content providers) will rather annotate content than develop ontologies, proper support is needed that allows annotating content in their native language. At the user interface level, millions of people would like to access relevant content in their native language irrespective of the source language in which annotations are presented. Any Semantic Web approach should include facilities to access information in several languages. Internationalisation and localization techniques should be explored to personalize information access based on the native language of the user. Challenge 4: Multilinguality

50. With the increasing amount of information overload, intuitive visualization of content will become more and more important, as users will be increasingly demanding easy recognition of the relevance of content for their purposes. The use of semantic indexes and routers for the storage, organization and finding of information, will require a major step forward in visualization, compared to traditional site maps that represent link structures. Techniques should allow for three-dimensional and new visualisation techniques to visualise SW content in any of the current SW languages. Technologies to be considered include X3D (of the Web3D Consortium), Java3D (API for writing programs to display and interact with three- dimensional graphics, Shockwave3D (technology introduced by Macromedia). Challenge 5: Visualization

51. The Semantic Web is an emerging field and the WWW consortium is producing recommendations on the languages and technology that will be used in this area. In order to advance the state of the art in the Semantic Web, it is important that such standards appear fast and will be adopted by the community. Challenge 6: Semantic Web Language Standardization

52. Architecture of the Semantic Web Technology

53. Semantic Web Companies (samples) ProfiumProfium (www.profium.com) develops Semantic Content Management Systems based on RDF Metadata and XML. OntologyWorks brings ontology-based information and enterprise software engineering tools to the commercial market. NetworkInference creating software products, and promoting the development of web standards, that, together, will power the advance of machine understanding, and reduce the level of human processing involved in web-based applications. CognIT is the Norway-based provider of CORPORUM, a tool suit for Ontologie Extraction, Semantic and Content Analysis, Summarising and Content Visualisation.www.profium.com OntologyWorks NetworkInference CognIT TaaleeTaalee provides semantics based search facilities. Invention-MachinesInvention-Machines provides also semantics based search facilities. AIdministratorAIdministrator develops semantic classification tools, plus software to visualise the results of semantic searches. OntopriseOntoprise develops Ontology Editors and Inference Engines. Intellidimension provides an RDF based information integration environment including an inference engine. Intellidimension

54. Summary: Semantic Web Concept & Applications (according to Dieter Fensel)

55. URI, HTML, HTTP Static WWW 500 million user more than 3 billion pages Concept

56. URI, HTML, HTTP Static WWW Serious Problems in information finding extracting representing interpreting and maintaining RDF, RDF(S), OWL Semantic Web Concept

57. Static Dynamic Bringing the computer back as a device for computation URI, HTML, HTTPRDF, RDF(S), OWL WWW Semantic Web UDDI, WSDL, SOAP Web Services Concept

58. Bringing the web to its full potential Static Dynamic UDDI, WSDL, SOAP Web Services URI, HTML, HTTPRDF, RDF(S), OWL WWW Semantic Web Intelligent Web Services Concept

59. The semantic web is based on machine- processable semantics of data. Its backbone technology are Ontologies. It is based on new web languages such as XML, RDF, and OWL, and tools that make use of these languages.

60. Ontologies are key enabling technology for the semantic web. They interweave human understanding of symbols with their machine processability. In a nutshell, Ontologies are formal and consensual specifications of conceptualizations that provide a shared and common understanding of a domain. Concept

61. Applications Knowledge Management Enterprise Application Integration eCommerce

62. Knowledge Management The competitiveness of companies in quickly changing markets depends heavily on how they exploit and maintain their knowledge. Increasingly, companies realize that their intranets are valuable repositories of corporate knowledge. To deal with this, several document management systems entered the market. However, these systems have severe weaknesses.

63. Knowledge Management Searching information: Existing keyword- based search retrieves irrelevant information that uses a certain term in a different meaning, and misses information when different terms with the same meaning about the desired content are used. Extracting information: Currently, human browsing and reading is required to extract relevant information from information sources and they need to manually integrate information spread over different sources.

64. Knowledge Management Maintaining weakly structured text sources is a difficult and time-consuming activity when such sources become large. Keeping such collections consistent, correct, and up-to-date requires mechanized representations of semantics that help to detect anomalies. Automatic document generation would enable adaptive websites that are dynamically reconfigured according to user profiles or other aspects of relevance.

65. Knowledge Management The Semantic Web will provide much more automated services based on machine-processable semantics of data, and on heuristics that make use of these metadata. Currently, we see many projects and products that are close to the market employing such concepts and ideas.

66. Enterprise Application Integration The integration of data, information, knowledge; processes; applications; and business becomes more and more important. Therefore, the Enterprise Application Integration area will have soon a major share of the overall spent IT expenses. A number of reasons are responsible for this trend.

67. Up to now, many companies trying to solve their integration needs by adhoc integration projects, however, adhoc integration do not scale. Therefore, after a phase of adhoc integration companies start to search for the Silver bullet that may help to solve the growing problem. However, global integration requires serious investments and time. Enterprise Application Integration

68. A successful integration strategy must combine the advantages of adhoc and global integration strategies: –Learning from adhoc integration means to make sure that we must reflect business needs as the driving force for the integration process; –Learning from global integration means to make sure that we must create extendable and reusable integrations. Enterprise Application Integration

69. Purpose-driven. We need to identify the major integration needs in terms of business processes and to structure our integration efforts around these needs. Extendable. We use Ontologies for publishing the information of data sources and for aligning it with business needs. By using Ontologies for making information explicit we ensure that our integration efforts can be extended in response to new and changed business needs. Reusable: Use web service technology to reflect further integration needs based on standardization. Web services as a vendor and platform independent software integration platform are of critical importance. Enterprise Application Integration

70. We expect that Enterprise Application Integration will be the major application are of Semantic Web technology before it will take the next logical step: => the integration of several organizations, i.e., eCommerce. Enterprise Application Integration

71. eCommerce eCommerce in business to business (B2B) is not a new phenomenon. However, the automatization of business transactions has not lived up to the expectations of its propagandists. Establishing a eCommerce relationship requires a serious investment and it its limited to a predefined number of trading partners.

72. Internet-based electronic commerce provides a much higher level of openness, flexibility and dynamics that will help to optimize business relationships. Anytime, anywhere, and anybody eCommerce provides completely new possibilities. eCommerce

73. Instead of implementing one link to each supplier, a supplier is linked to a large number of potential customers when he is connected to the marketplace. A supplier or customer can change its business relationships reflecting new demands from his market. This enables virtual enterprises and vica versa it enables to brake large enterprises up into smaller pieces that mediate their eWork relationship based on eCommerce relationships. eCommerce

74. However, enabling flexible and open eCommerce has to deal with serious problems. Heterogeneity in the product, catalogue, and document description standards of the trading partner. Effective and efficient management of different styles of description becomes a key obstacle for this approach. eCommerce

75. Openness of eCommerce cannot be achieved without standardization. This we can learn from the web! Here, we also require standardization of the actual content, i.e., we require Ontologies. eCommerce: Opennes

76. Flexibility of eCommerce cannot be achieved without multi-standard approaches. Ontology need to be implemented as networks of meaning where from the very beginning, heterogeneity is an essential requirement for this Ontology network. Tools for dealing with conflicting definitions and strong support in interweaving local theories are essential in order to make this technology workable and scalable. eCommerce: Flexibility

77. Dynamic of eCommerce requires standards that act as living entities. Products, services, and trading modes are subject of high change rates. Ontologies are used as a means of exchanging meaning between different agents. They can only provide this if they reflect an inter- subjectual consensus. By definition, they can only be the result of a social process. eCommerce: Dynamic

78. –For this reason, Ontologies cannot be understood as a static model. –An Ontology is as much required for the exchange of meaning as the exchange of meaning may influence and modify an Ontology. –Consequently, evolving Ontologies describe a process rather than a static model. –Ontologies must have strong support in versioning and must be accompanied by process models that help to organize evolving consensus. eCommerce: Ontologies

79. Summary: Risc vs. Impact Tradeoff Impact Risc Knowledge Management Enterprise Application Integration eCommerce

80. Heterogeneity... … is a Babel Tower!! SEMANTIC INTEROPERABILITY metadata ontologies contexts SEMANTIC HETEROGENEITY

81. The first Semantic Web Kick-Off Meeting in Finland was in Helsinki 2 November 2001; Later Finnish portal on Semantic web activities was launched in http://www.cs.helsinki.fi/u/eahyvone/stes/semanticweb. Semantic Computing (SeCo) research group was formally established in the spring 2002. The group belongs to the University of Helsinki, Department of Computer Science and Helsinki Institute for Information Technology (HIIT). Group leader is Prof. Eero Hyvonen The first projects focus on Semantic Web and Web Service applications and representation of cultural content on the Web. Semantic Web Activities in Finland

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

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

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

85. 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.

86. 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.

87. 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.

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

89. Mobile Location-Based Service in Semantic Web

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

91. 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

92. 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.

93. 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,

94. 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,

95. 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

96. 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.

97. 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

98. 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 !!!

99. 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”

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

101. 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)

102. Intelligent Query Routing in P2P Environment history Semantically enriched query package Adding extra knowledge to query package peers make its routing in the network more intelligent. Adding extra knowledge about neighbors in a history database of a peer enables intelligent routing. Peers having similar experience can help other peers to find appropriate service.

103. EDUTELLA semantically annotated data repository semantic query (RQL, RDF-QEL-i ) EDUTELLA project is a multi-staged effort to scope, specify, architect and implement an RDF-based metadata infrastructure for P2P-networks based on the recently announced JXTA framework. http://edutella.jxta.org/

104. 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

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

106. 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 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

107. 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.

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

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

110. 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

111. 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

112. 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

113. 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

114. 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

115. 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

116. 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.

117. 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

118. 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

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

120. OntoGames : Semantic Games Space Personal ontology General ontology

121. 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

122. 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

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

124. 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

125. 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

126. 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

127. 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

128. Industrial Ontologies Group: Examples of Related Contacts

129. University of Jyvaskyla Experience: Examples of Related Courses

130. 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.

131. Developing ontology languages, ontologies, annotation support tools will give you an advance of several years before others can develop the same. Important is that the standards and the applications will depend on you. Developing Semantic Web service platforms, agents, applications, based on widespread standards allows to automatically explore rich Web content providing services for millions of customers. Annotate your own products and services. This makes your products and services reachable by new generation of semantic search engines and automatically accessed by Web applications, agents and services. Company Benefits from the Semantic Web

132. 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)

133. “Ask not what the Semantic Web Can do for you, ask what you can do for the Semantic Web” Hans-Georg Stork, European Union http://lsdis.cs.uga.edu/SemNSF