Semantic Web State-of-Art and Opportunities

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					 Industrial Ontologies Group

                               Semantic Web:
State-of-Art and Opportunities

                    “Industrial Ontologies” Group

                               University of Jyväskylä, August 2003
     Our Team: “Industrial Ontologies” Group

                                              • Head:
                                                   – Vagan Terziyan
                                              • Researchers:
                                                   –   Oleksandr Kononenko
                  MIT Department,                  –   Andriy Zharko
                  University of
                  Jyväskylä                        –   Oleksiy Khriyenko
                                                   –   Olena Kaykova
                                                   –   …
                                              • Consultant (Metso Oy):
                                                   – Jouni Pyotsia
                                              • Manager (Science Park):
                                                   – Mikko Kovalainen
“Industrial Ontologies” Group:
        “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);
          “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;
          “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.
           Industrial Ontologies Group:
               Important Objective

• For us there are no doubts about the
  possibilities, which Semantic Web opens for
• 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.
            Why and Where Semantic Web ?

                 more then 3,000,000,000 web-pages
  WWW            “Information” burst
                 ICT needs comprehensive resource management technology

                 Needs for integration of businesses
 Business        Web Services for e-Business
                 Standardization and Interoperability problems

Knowledge        Consolidate and reuse experience
Management       Standardize knowledge sharing technology
                 Needs for the intelligent tools to use human‟s knowledge
                                  Motivation for Semantic Web

Before Semantic Web                            Semantic Web Structure

                                                                              Ontologies   Logical Support

                                                                                Tools      Applications /

WWW      Creators                  Users
                                                WWW         Creators                              Users
and                                             and
Beyond              Web content                                             Web content

                                           7                                                                 8
     What is the “Transactional

• 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.
    Summarizing the Problem:
 Computers don‟t understand Meaning

• “My mouse is broken. I need a
  new one…”
An Example
             Use of ontology
             “My mouse is broken”
             vs. “My mouse is dead”
           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”

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.
                      Word-Wide Correlated Activities

                             Semantic Web                                   Global, collaborative effort
                                                                  to construct an open network of on-line systems
                                                                       hosting diverse agent based services.
    Extending current web by giving information
    a given well-defined meaning, better enabling
    computers and people to work in cooperation
   Grid Computing
    Utilizing the global Internet to build
distributed computing and communications

                                                                     Producing standards for the interoperation
                 Web Services                                            of heterogeneous software agents.

        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.
         Web Migration to New Technology
                “In 30 years e-commerce will have become
                second nature. Lifelike, intelligent virtual
                assistants will be performing most routine
                transactions and simple negotiations
100%            electronically on our behalf. More
                technological change will have taken place in
                that period than during the entire twentieth
         HTML   century, and the curve will continue to
                steepen exponentially into the foreseeable
                future.” Ray Kurzweil

                                    “Fifty percent of the content on
                                    the Web will be in XML format
                                    by the end of 2003”
                                    ……….Gartner Group
  2000                       2005                               2010
Tim Berners-Lee's Vision of
 Semantic Web (IJCAI-01)
               Semantic Web: New “Users”

            Creators                               Users
Web and                     Semantic Web                     applications
Beyond                         content

                              Ontologies   Logical Support

                                Tools      Applications /

WWW         Creators                              Users
Beyond                      Web content
               Professions around Semantic Web

     Content creators                  AI Professionals

                            Content                       Logic, Proof
Mobile Computing                                           and Trust

                                                              Web designers

              Agents                                 Annotations

                                             Ontology engineers
           Software engineers
                 Semantic Web: Resource Integration


  Web resources /
services / DBs / etc.
                  Semantic Web: What to Annotate ?
                                                      External world
  Web resources /
services / DBs / etc.

  Web users
    (profiles,               Shared
  preferences)               ontology

                                                      Web agents /

        Web access
         devices                        Smart
                                        and devices
The Semantic Web
Can’t we just use XML?
This is what a web-page in natural language
looks like for a machine

                                      J. Hendler
                  XML helps

XML allows “meaningful tags” to be added to
parts of the text

                                         < name >

< education>

                                     < CV >
    < work>

  < private >

                                              J. Hendler
   XML  machine accessible meaning
        But to your machine,
        the tags look like this….

                                        < name >

< education>

                                    < CV >
    < work>

  < private >

                                             J. Hendler
Schemas take a step in the right
Schemas help….

                 < CV >    …by relating
                 private   common terms
                           between documents

                                    J. Hendler
 But other people use other schemas

        Someone else has one like this….

                                           < name >

< education>

                                     < CV
                                     < CV >>
    < work>

  < private >

                                               J. Hendler
The “semantics” isn’t there

           < CV >
          private   …which don’t fit in

                                J. Hendler
       KR provides “external”
       referents to merge on

                                work         CV

                         educ          CV


Semantic Web languages add
mappings and structure.                           J. Hendler
             Semantic Web basics…
 RDF:
   • is a W3C standard, which provides tool to describe Web
   • 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
  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
            Ontologies: the foundation of Semantic Web

                                        public                 comment                __Thing__                    Author
Ontologies are key                   private                                        is-a
                                                  Access Rights                                                   Location
enabling technology for
the Semantic Web                                    Related to                   Document                                  name
                                                         Report            is-a        is-a
“..explicit specification of                                                                   Web-page                        uri
conceptualization..”                              Subject

                                                                   Instance-of                      Instance-of
                                                                                                                       O. Kononenko
Ontology is formal and rich                                                           V. Terziyan
way to provide shared and                                                                  Author
                                                  Access rights
common understanding of                                 name             #doc1                               #doc2
                                                                                           Related to
a domain, that can be used         Semantic Web
                                                          Location                                                   uri
by people and machines                \\AgServ\vagan\InBCT_1.doc
                                               3.1: analysis
                                                                            draft                        Home page

  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
              Semantic Web: Interoperability
                        Ontology A: Documents Ontology B: Research
                                                      Ontology C: Services

                                                                              System 2
 System 1

                                            Semantic   Instance-of   V. Terziyan
A commitment to a common                      Web
ontology is a guarantee of a                      A:name                     A:Author
consistency and thus possibility                                             3.1: analysis
of data (and knowledge) sharing                  A:Location      A:Subject
Co-operative Work in Web

Co-operative Work in Semantic Web

        Semantic Web

Semantic Web is not Only ...
… but Also ...
     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
         The Web Services Stack

Wire Protocol              Description                Discovery

     SOAP                      WSDL                 Registry (UDDI)

                                                provides a standard, flexible
                               provides a          way to discover where a
    provides a
                           standard, flexible   Web service is located and
 standard, flexible
                            way to describe          where to find more
                            what and how a       information about what the
                           Web service does           Web service does
                              what it does

interoperability at   interoperability at              dynamic
the lowest level      the content level                discovery
Six Challenges for the Semantic Web

       Richard Benjamins, Jesus Contreras,
       Oscar Corcho, Asuncion Gomez-Perez

                    April 2002
        Challenge 1: Availability of Content

• 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 2: Ontology Availability,
           Development and Evolution
• 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,

• 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
          Challenge 3: Scalability of Semantic
                     Web Content
• 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
               Challenge 4: Multilinguality

• 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 5: Visualization

• 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 6: Semantic Web
              Language Standardization

• 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.
Architecture of the Semantic Web
        Semantic Web Companies (samples)
Profium ( 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
Taalee provides semantics based search facilities.
Invention-Machines provides also semantics based search facilities.
AIdministrator develops semantic classification tools, plus software to visualise
the results of semantic searches.
Ontoprise develops Ontology Editors and Inference Engines.
Intellidimension provides an RDF based information integration environment
including an inference engine.

Semantic Web Concept & Applications
         (according to Dieter Fensel)

                             500 million user
                     more than 3 billion pages

Static   URI, HTML, HTTP

         Serious Problems in information
                       •and maintaining

         WWW                       Semantic Web
Static   URI, HTML, HTTP            RDF, RDF(S), OWL

                            Bringing the computer
                            back as a device for
          Web Services      computation

          WWW                    Semantic Web
 Static   URI, HTML, HTTP          RDF, RDF(S), OWL

           Bringing the web to its full potential

          Web Services                Intelligent Web

          WWW                         Semantic Web
 Static   URI, HTML, HTTP              RDF, RDF(S), OWL

• 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.

• 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.

•   Knowledge Management

•   Enterprise Application Integration

•   eCommerce
      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.
          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.
         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.
      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.
       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
       Enterprise Application Integration

•   Up to now, many companies trying to solve
    their integration needs by adhoc integration
    projects, however, adhoc integration do not
•   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

•   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

    – Learning from global integration means to
      make sure that we must create extendable
      and reusable integrations.
    Enterprise Application Integration

•   Purpose-driven. We need to identify the major
    integration needs in terms of business processes and
    to structure our integration efforts around these
•   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
       Enterprise Application Integration

•   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.

•   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.

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

•   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
•   A supplier or customer can change its business
    relationships reflecting new demands from his
•   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.

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

•   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
           eCommerce: Flexibility

•   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: Dynamic

•   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: Ontologies

– 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
– 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
     Summary: Risc vs. Impact Tradeoff



                    Enterprise Application Integration

           Knowledge Management

Heterogeneity...                   … is a Babel Tower!!





         Semantic Web Activities in Finland

• 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
• 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
 Industrial Ontologies Group
  Samples of our Research:

“Applications of Semantic Web”
            Web Resource/Service Integration:
            Server-Based Transaction Monitor

     resource /

Server                                          Client

TM                                    Web
                                   resource /

Transaction Service                Server
    Web Resource/Service Integration:
   Mobile Client-Base Transaction Monitor

resource /    wireless



                           resource /

                                                    Client 1                                                                         Client r
                                    Transaction data                  Services data                                 Transaction data                   Services data

                                  Parameter 1 Recent value           Service 1 ********                           Parameter 1 Recent value            Service 1 ********

                                  Parameter 2 Recent value           Service 2 ********                           Parameter 2 Recent value            Service 2 ********

                                      …              …                         …                                      …              …                          …
                                  Parameter n Recent value           Service s ********                           Parameter n Recent value            Service s ********

 The conceptual scheme
                                                  Transaction monitor
                                                                                             …                                 Transaction monitor

  of the ontology-based
    management with                                                                       Ontologies

                                                                                                                   Service atomic action ontologies

   multiple e-services
                                                   Parameter ontologies

                                                                                               input parameters              input parameters            input parameters
                                    Parameter 1    Name 1     Default value / schema 1

                                    Parameter 2    Name 2     Default value / schema 2

                                    …              …          …
                                                                                             Name of action 1             Name of action 2           Name of action k

                                    Parameter n    Name n     Default value / schema n                                                          …
                                                                                              output parameters             output parameters           output parameters

Terziyan V., Ontological
Modelling of E-Services to
Ensure Appropriate Mobile                         Service 1                                                                     Service s

Transactions, In: International              Subtransaction monitor                                                          Subtransaction monitor

Journal of Intelligent Systems
in Accounting, Finance and           Service Tree                  Clients data

                                                                   Client 1 ********
                                                                                                                    Service Tree                    Clients data

                                                                                                                                                    Client 1 ********

Management, J. Wiley & Sons,                                       Client 2 ********

                                                                                                                                                    Client 2 ********


                                                                   Client r ********                                                                Client r ********

Vol. 12, 2003, 14 pp.
                              Ontology-Based Transaction
                            Management for the Semantic Web
Web Resource/Service Integration:                                Consider two basic transaction management architectures in
                                                                 mobile environment depending on where the Transaction
Mobile Client-Base Transaction Monitor
                                                                 Monitor (TM) will be located. First one (Server-Based)
                                       TM                        assumes that TM will be located in server side, e.g. within
     resource /
                      wireless                                   some transaction management service. Second one (Client-
                                                                 Based) supposes that TM is located in mobile client terminal.

                                     wireless                    The first objective will be to provide and study an
                                                                 integrated mobile transaction management architecture
                                                                 for the Semantic Web applications, which will combine
                                      resource /                 the best features from these two architectures by
                                                                 intelligent switching from one architecture to another
                                     Server                 22
                                                                 one depending on current application context.
                                                                 There is already some ontological support for Semantic Web
Web Resource/Service Integration:                                resources and services interoperability based on OWL,
Server-Based Transaction Monitor                                 DAML-S. However to be able to manage transactions in
                                                                 Semantic Web across multiple resources (or services) there
     resource /                                                  will not be enough only ontologies for semantic annotations
                                                                 of these resources; there will be evident need of the ontology
Server                                             Client
                                                                 for the Semantic Web transactions itself.
                                                                 The second objective will be developing pilot ontology
                                                                 for the RDF-based semantic annotation of mobile
TM                                       Web
                                      resource /
                                                                 transactions in the Semantic Web.

Transaction Service                  Server                 21
                               Architecture for a Mobile P-Commerce Service

  Clients                              Server                                                   External
   Public merchants,
public customers, public                                Maps             Maps                         …
 information providers                             <path network>   <business points>

                                                                                              Map Content
                                                 SMOs          Integration,                     Server
                           I            I                       Analysis,
                                                                Learning                       Location
                           C            S        SMRs
                           I    XML     I
                                WML                                   Business
                                                                     knowledge          XML          …
                     Meta-            Profiles
                    Profiles                                                                    Content
      …                                                                                        Providers
                                                               Negotiation,                     Server
                                                                 Billing                            …

                                                                                              $ $ $ Banks

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

                                                                     Service User
                              Service User

Service User

                                                                               Service User

           Service User
                                                      Service User
                        Mobile Location-Based
                       Service in Semantic Web
M-Commerce LBS system                                                   Adaptive interface for MLS client
                         In the framework of the Multi Meet Mobile
                         (MMM) project at the University of Jyväskylä,
                         a LBS pilot system, MMM Location-based
                         Service system (MLS), has been developed.
                         MLS is a general LBS system for mobile
                         users, offering map and navigation across
                         multiple geographically distributed services
                         accompanied with access to location-based
                         information through the map on terminal‟s
                         screen. MLS is based on Java, XML and uses
                         dynamic selection of services for customers
                         based on their profile and location.

                         Virrantaus K., Veijalainen J., Markkula J.,
                         Katasonov A., Garmash A., Tirri H., Terziyan V.,
                         Developing GIS-Supported Location-Based
                         Services, In: Proceedings of WGIS 2001 - First
                         International Workshop on Web Geographical
                         Information Systems, 3-6 December, 2001, Kyoto,
                                                                            Only predicted services, for the customer with known profile
                         Japan, pp. 423-432.                                and location, will be delivered from MLS and displayed at
                                                                            the mobile terminal screen as clickable “points of interest” 20

Route-based personalization

  Static Perspective          Dynamic Perspective                21
       Machine-to-Machine Communication


Heterogeneous machines can
“understand” each other while
exchanging data due to shared
  Semantic Web-Supported Sharing and
      Integration of Web Services

                      Different companies would be
ontology              able to share and use
                      cooperatively    their   Web
                      resources and services due to
                      standardized descriptions of
                      their resources.
                          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
                  What parties can do:
                                                     business environment for their applications.
                                            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 / Cooperative Semantic Web of shared resources
               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,
           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,
       Embedded Maintenance Platforms


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

    Service Agents
            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.
                     Agents in Semantic Web

      1. “I feel bad, pressure          3. “Wait a bit, I
          more than 200,                 will give you
      headache, … Who can                 some pills”
       advise what to do ? “

           Agents in Semantic Web supposed
           to understand each other because
            they will share common standard,
             platform, ontology and language

                                                        4. “Never had such
 2. “ I think you                                       experience. No idea
should stop drink                                           what to do”
beer for a while “
      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 !!!
             GUN Concept
                              2. “I have some
                               pills for you”

     1. “I feel bad,
temperature 40, pain in
 stomach, … Who can
  advise what to do ? “

 Entities will interoperate
through OntoShells, which
are “supplements” of these
entities up to Semantic Web
       enabled agents
Semantic Web: Before GUN

        Semantic Web Applications

    Semantic Web applications
   “understand”, (re)use, share,
   integrate, etc. Semantic Web

         Semantic Web Resources
            GUN Concept:   All GUN resources “understand” each other

Real World Object +
                                                        Real World
 + OntoAdapter +                                         objects
  + OntoShell =
= GUN Resource



                                                  Real World objects
                                                  of new generation
                                                   (OntoAdapter inside)
         Intelligent Query Routing in P2P Environment


                 enriched query

 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.

  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.

         Interoperability of Heterogeneous Software

                                                     Dynamic Link

          Java package      (Semantic)

 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.
 Industrial Ontologies Group
        Future Plans:

“Applications of Wireless Semantic Web”
                      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                mapping between views
                  model (ontology)                                               data semantic descriptions
Personal                                      Semantic Web                                             Commitment
data-view                                       Inside™                                                to ontology


                                  User data becomes available to variety of
     My resources                 applications and other people
                                                                                                Other people’s
 and their descriptions                                                                          data-views
                      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 languages
  Model of user‟s data and other resources:                      it will be stored in universal data format.
  - 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
Building own data model…

                   added slot (property/field)
                   inherited slot
              Building own data structure

“Relative is a kind
    of friend”

                       Inherited properties

                                              added slot (property/field)
                      Links to other          inherited slot
                       data entities
Building own data structure

   Customized data model:
 • new kinds of data
 • new kinds of representation
 • rules and constraints for data etc.
 • association of data with applications

                                           added slot (property/field)
                                           inherited slot
                              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!
                   Access your data quickly and easily…

                                                                Possibilities to build flexible,
                                                                easily customizable data
                                                                management applications are

                                                 Event data

                                           Just click to open

                                                                          Terziyan’s Contact data

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
                       Customizable personal information
                           management environment
                                                               Easy-to-use, flexible, customizable
                                                                  data management for users
Personal data “view”:
•   Development of own view on personal data                                            Repositories of ready
•   Reusing of existing views (join, modify, extend)
•   Links between personal and some “global” ontology
                                                                                       Enabled collaboration and
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

       Note: Protégé-2000 ontology development and knowledge acquisition tool was used for demonstration
General ontology

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

   Context and preferences-
      based adaptation
     Support for semi-natural
   Effective filtering of wide variety
           of Web-resources
   Technology that supports future
     Ubiquitous Semantic Web
               Agent-to-Agent communication

                                    Semantic annotation of
Phone calls are also                the local data enables its
possible between mobile             intelligent processing by
                                    software.      Ontologies
terminal agents. They
                                    provide interoperability
are performed without
                                    between heterogeneous
human participation in              peers.
order to exchange local
             Agent-to-Agent communication
         semantics enables
       intelligent data processing
      ontological relations                          Business
     define possible                 Cooking
    cooperation between
   domain agents
  shared ontology                                     Health

                                                                                                              Health Center

                                                                 Remote Health Maintenance Center
                                                    On a beach

                   and                                                           “Recovery” Agents    “Therapist”

  Local Health Maintenance Center

 Anywhere                                                                                                           Maintenance Crew
                                “Therapist”                                     “Diagnostic” Agents
            “Recovery” Agents


     Fishing Agents
                                                                           Health Maintenance
                                                                            without barriers
                                                                                Anytime and Anywhere
                    In the office
          OntoGames: New Games Generation
Personal ontology                                       General ontology

       Personal User Profile               Common Games Profile

                               PUP   CGP
             OntoGames: Semantic Games Space
Personal ontology                       General ontology
            OntoGames: Exit in the Real Life
Personal ontology                                   General ontology

                    Non Stop Game - Non Stop Life

                          CONNECTING PEOPLE
                                 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
                                                                  Processing of data by some other
                                                                  semantic-aware applications
           BANK: Customer’s data processing


                                        Clients clustering

                          Input forms


                  BANK: Services annotation
                                                                           annotated bank
Semantics enabled services –
easy way to use for customer


                        I want to …
                                                          Information filing,
                        Less detailed                     all documentation
                        information                        and transactions
             BANK: Loan Borrower annotation

               Bank - investor

      Automated support of:
• making decisions about trusting
• prediction of future trends
  via semantically annotated loan
      borrowers information

                                    Loan borrowers
                        Read Our Recent Reports

• Semantic Web: The Future Starts Today
    – (collection of research papers and presentations of Industrial Ontologies
      Group for the Period November 2002-April 2003)                    V. Terziyan

• Semantic Web and Peer-to-Peer:
  Integration and Interoperability in Industry
                                                                           A. Zharko

• Semantic Web Enabled Web Services:
  State-of-Art and Challenges
                                                                        O. Kononenko
• Distributed Mobile Web Services Based on Semantic Web:
  Distributed Industrial Product Maintenance System

                                                                         O. Khriyenko
• Available online in:

                                                                               Industrial Ontologies Group
              Semantic Web: The Future starts today

                     Interoperability standards




                                                  “Web Of Trust”
                             Industrial Ontologies Group: Examples
                                       of Related Contacts
 Participation in
 OntoWeb Network
The goal of the OntoWeb Network is to bring
together researchers and industrials coming
from the research and applications areas,
promoting interdisciplinary work and
strengthening the European influence on
Semantic Web standardisation efforts such as
those based on RDF and XML. Europe's
cultural diversity and multi linguality, together
with the strong scientific competencies
existing in the ontology field, may give Europe
a unique opportunity to fully exploit ontology-
based technology and to play a leading role in
these emerging area.                                                             24

 University of Jyvaskyla                                                                IT Faculty (Jyvaskyla) - AI Department (Kharkov):
 is Member of WIM                                                                       Ukrainian students in Mobile Computing Line

 Wireless Information Management (WIM) is a           Students Background                                 Special personal abilities
 research training network involving                                                    -C and JAVA Programming
 researchers from six universities in Denmark,                                                                                              -strong motivation
                                                                                        -Network Management
 Finland, Lithuania, Norway, and Sweden.                                                                                                    -analytical thinking
 Approximately 30 Ph.D. students and their                                              -Mobile Technologies
 advisors and colleagues take part. Wireless                                            -Intelligent Agents Technologies
 Information Management encompasses the                                                                                                     -self-learning
                                                    1. Aalborg University               -Web-content Filtering and Personalization
 management of information obtained from                                                                                                    -knowledge acquisition
                                                    2. Norwegian University of
 sensors as well as the management of                                                   -Data and Web Mining                                -flexibility
 information involving mobile objects, both of
                                                       Science and Technology
                                                    3. University of Jyvaskyla          -Knowledge Management                               -professionalism
 which types of information concern continuous
 change, be it in virtual spaces or physical        4. Uppsala University               -Semantic Web (XML, RDF, RDF Schema,
 space. These types of data will gain               5. Vilnius Gediminas Technical      DAML)
 prominence in step with the increasing                University
 deployment of wireless communications and                                              -Mobile Commerce Applications
                                                    6. Agder University College                                            
 sensor technologies.                                                                   -Object-oriented Design
 The project aims to offer training in this area,
                                                                                        -Database Management
 in which there are significant industrial
 strengths and interests in the Nordic region.                                   25                                                                                     26
University of Jyvaskyla Experience:
   Examples of Related Courses

       Digitaalisen median erityiskysymyksiä (2 ov)                 Structured Electronic Documentation
                          seminaarin aihepiiri:
                     Semanttinen web
                                                                                 Lecturer: Matthieu Weber
                     Lecturer: Airi Salminen
                                                                    University of Jyvaskyla, MIT Department, Fall 2001, 2002
        University of Jyvaskyla, CS & IS Department, Spring 2002
                                                               18                               18
                       Cooperation with American Universities
                    Ioannis Kakadiaris                                                  John Canny
                    Ass. Professor,                                                     Professor,
                    Department of                                                       Division of Computer
                    Computer Science,                                                   Science, University of
                    University of                                                       California, Berkeley,
                    Houston, USA                                                        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.                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
Cooperation focuses to investigating issues related to
                                                             and a PYI while at Berkeley. He developed inexpensive,
management of the Web content which includes human
                                                             ubiquitous telepresence robots called "PRoPs”...
motions as its component, according to the common
framework of management multimedia content in the
Semantic Web. Possible applications considered:              Cooperation focuses to following subjects:

- Automatic remote camera control (behavior                  - Knowledge management of a community of trust;
recognition, intentions capture, operator (astronaut)        - Collaborative Filtering with Privacy;
actions control etc.)                                        - Intelligent Integration of Filtering Models;
                                                             - Adaptive User Interfaces;
- Semantic video transmission (transmit wireless only        - Human-Centered Computing;
recognized semantics of motions).                            - Online Collaborative learning.
        Company Benefits from the Semantic Web

• 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.
• Semantic Web is not only a technology as many
  used to name it;
• Semantic Web is not only an environment as
  many naming it now;
• Semantic 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 (tel. +358 14 2604618)
 “Ask not what the Semantic
Web Can do for you, ask what
you can do for the Semantic
           Hans-Georg Stork, European Union

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