Semantic Web:Semantic Web:State-of-Art and OpportunitiesState-of-Art and Opportunities

“Industrial Ontologies” Group

http://www.cs.jyu.fi/ai/OntoGroup/index.html

University of Jyväskylä, August 2003

Industrial Ontologies GroupIndustrial Ontologies Group

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

““Industrial Ontologies” Group: Industrial Ontologies” Group: Our HistoryOur History

• 1978-1984 – We took part in development of the first in USSR Industrial Natural Language Processing System “DESTA”, which included semantic analysis and ontologiesontologies;

• 1985-1989 - We took part in development of the first in USSR Industrial Automated Natural Language Programming System “ALISA”, which Enabled Semantic AnnotationEnabled Semantic Annotation, DiscoveryDiscovery and IntegrationIntegration of software components (prototype of today's Semantic Web ServicesSemantic Web Services concept);

““Industrial Ontologies” Group: Industrial Ontologies” Group: Our HistoryOur 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 WebSemantic 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: Industrial Ontologies” Group: Our HistoryOur 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 Semantic WebWeb concepts.

Industrial Ontologies Group:Industrial Ontologies Group:Important ObjectiveImportant 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.

Why and Where Semantic Web ?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

4

Web Limitations

Doubles in sizeevery six months

Average WWW searches examineonly about 25% of potentially

relevant sites and return a lot ofunwanted information

Information on web is not suitablefor software agents

World Wide Web

Semantic Web

The Semantic Web is avision: the idea of havingdata on the Web defined andlinked in a way that it can beused by machines not just fordisplay purposes, but forautomation, integration andreuse of data across variousapplications.

7

B e f o r e S e m a n t i c W e b

W e b c o n t e n t

U s e r sC r e a t o r sW W Wa n dB e y o n d

8

S e m a n tic W e b S tru c tu re

S e m a n ticA n n o ta tio n s

O n to lo g ie s L o g ic a l S u p p o rt

L a n g u a g e s T o o ls A p p lic a tio n s /S e rv ic e s

W e b c o n te n t

U se rsC re a to rsW W Wa n dB e y o n d

S e m a n ticW e b

Motivation for Semantic Web Motivation for Semantic Web

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.

Summarizing the Problem: Computers don’t understand Meaning

• “My mouse is broken. I need a new one…”

An ExampleUse of ontology

“My mouse is broken” vs. “My mouse is dead”

Approach: Semantic WebApproach: 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.

Word-Wide Correlated ActivitiesWord-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 throughthe use of loosely coupled, reusable software components.

FIPA

Producing standards for the interoperation of heterogeneous software agents.

Extending current web by giving informationa given well-defined meaning, better enablingcomputers and people to work in cooperation

Utilizing the global Internet to builddistributed computing and communications

infrastructures.

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 TechnologyWeb Migration to New Technology

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

Semantic Web: New “Users” Semantic Web: New “Users”

SemanticAnnotations

Ontologies Logical Support

Languages Tools Applications /Services

Web content

UsersCreatorsWWWandBeyond

SemanticWeb

Semantic Webcontent

UsersSemanticWeb andBeyond

Creators

applications

agents

Content

Agents Annotations

Ontologies

Software engineersOntology engineers

Web designers

Content creators

Logic, Proof and Trust

AI Professionals

Mobile Computing Professionals

Professions around Semantic WebProfessions around Semantic Web

Semantic Web: Resource IntegrationSemantic Web: Resource Integration

Shared ontology

Web resources / services / DBs / etc.

Semantic annotation

Semantic Web: What to Annotate ?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

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

J. Hendler

XML helps

CV

name

education

work

private

< >

< >

< >

< >

< >

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

J. Hendler

XML machine accessible meaning

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

But to your machine, the tags look like this….

J. Hendler

Schemas take a step in the right direction

Schemas help….

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

< > …by relating common termsbetween documents

But other people use other schemas

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

>

<>

<>

Someone else has one like this….

J. Hendler

The “semantics” isn’t there

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

< >…which don’t fit in

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

< >

< >

J. Hendler

KR provides “external” referents to merge on

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

<>

<>

<>

Semantic Web languages add mappings and structure.

CV

name

education

work

private

< >

< >

< >

< >

< >

< >

< >

< >

< >

CV

name

education

work

private

< >

< >

< >

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< >

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< >

< >

< >

CV

name

education

work

private

< >

< >

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< >

< >

< >

CV

name

education

work

private

< >

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CV

name

education

work

private

< >

< >

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J. Hendler

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

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 WebOntologies: 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 Instance-of

Query 1: get all documents from location X, but not web-pagesQuery 2: get documents related to Y, with more then one author, one of which is TerziyanQuery 3: are there web-pages of Z with “private” access related to documents with subject S?

Related to

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 Webname

public

private

Semantic Web: InteroperabilitySemantic Web: InteroperabilityOntology A: Documents Ontology B: Research

A commitment to a common ontology is a guarantee of aconsistency and thus possibility of data (and knowledge) sharing

Common (shared) ontology

Ontology C: Services

System 1System 2

\\AgServ\vagan\InBCT_1.doc

V. Terziyan

A:Report

A:Location3.1: analysis

A:Subject

A:Author

Instance-ofSemantic Web

A:name

Co-operative Work in WebCo-operative Work in Web

WWW

Co-operative Work in Semantic WebCo-operative Work in Semantic Web

WWW

Semantic Web

Semantic Web is not Only ...Semantic Web is not Only ...

… but Alsobut 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 provides a standard, flexible standard, flexible communications communications

channelchannel

provides a provides a standard, flexible standard, flexible way to describe way to describe what and how a what and how a

Web service does Web service does what it doeswhat it does

provides a standard, flexible provides a standard, flexible way to discover where a way to discover where a

Web service is located and Web service is located and where to find more where to find more

information about what the information about what the Web service doesWeb service does

interoperability at interoperability at the lowest levelthe lowest level

interoperability at interoperability at the content levelthe content level

dynamic dynamic discoverydiscovery

Six Challenges for the Semantic Web

Richard Benjamins, Jesus Contreras,

Oscar Corcho, Asuncion Gomez-Perez

April 2002

• 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 ContentChallenge 1: Availability of Content

• 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, Challenge 2: Ontology Availability, Development and EvolutionDevelopment and Evolution

• 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 Challenge 3: Scalability of Semantic Web ContentWeb Content

• 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: MultilingualityChallenge 4: Multilinguality

• 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: VisualizationChallenge 5: Visualization

• 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 Challenge 6: Semantic Web Language StandardizationLanguage Standardization

Architecture of the Semantic Web Architecture of the Semantic Web TechnologyTechnology

Semantic Web Companies Semantic Web Companies (samples)(samples)

Profium (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.

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.

Summary:Summary:

Semantic WebSemantic Web Concept & Applications Concept & Applications(according to Dieter Fensel)(according to Dieter Fensel)

URI, HTML, HTTPStaticWWW

500 million usermore than 3 billion pages

Concept

URI, HTML, HTTPStaticWWW

Serious Problems in information•finding

•extracting•representing•interpreting

•and maintaining

RDF, RDF(S), OWLSemantic Web

Concept

Static

Dynamic

Bringing the computer back as a device for computation

URI, HTML, HTTP RDF, RDF(S), OWL

WWW Semantic Web

UDDI, WSDL, SOAP

Web Services

Concept

Bringing the web to its full potential

Static

Dynamic UDDI, WSDL, SOAP

Web Services

URI, HTML, HTTP RDF, RDF(S), OWL

WWW Semantic Web

Intelligent Web Services

Concept

Concept

• 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.

Concept

Applications

• 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 trend.

• 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

• 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

• 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

• 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

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 relationships.

• Anytime, anywhere, and anybody eCommerce provides completely new possibilities.

eCommerce

• 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

• 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

• 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

• 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

• 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

– 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

Summary: Risc vs. Impact Tradeoff

Impact

Risc

Knowledge Management

Enterprise Application Integration

eCommerce

Heterogeneity...Heterogeneity... … … is a Babel Tower!!is a Babel Tower!!

SEMANTIC INTEROPERABILITYSEMANTIC INTEROPERABILITY

metadata

ontologies

contexts

SEMANTIC HETEROGENEITYSEMANTIC HETEROGENEITY

• 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

Industrial Ontologies GroupSamples of our Research:

“Applications of Semantic Web”

Web Resource/Service Integration:Web Resource/Service Integration:Server-Based Transaction MonitorServer-Based Transaction Monitor

Server Client

Server

Webresource /

service

Webresource /

service

Transaction Service

TMTM

wireless

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

MonitorMonitor

ServerClient

Server

Webresource /

service

TM

Webresource /

service

wireless

wireless

The conceptual scheme The conceptual scheme of the ontology-based of the ontology-based

transaction transaction management with management with multiple e-servicesmultiple e-services

Transaction data

Service 1 ********

Service 2 ********

…

Service s ********

Services data

Transaction monitor

Client 1

…

Service 1 ********

Service 2 ********

…

Service s ********

Services data

Transaction monitor

Client r

Parameter 1

Parameter 2

…

Parameter n

Recent value

Recent value

…

Recent value

Transaction data

Parameter 1

Parameter 2

…

Parameter n

Recent value

Recent value

…

Recent value

Service atomic action ontologies

Parameter 1

Parameter 2

…

Parameter n

Parameter ontologies

Ontologies

Name 1

Name 2

…

Name n

Default value / schema 1

Default value / schema 2

…

Default value / schema n

Name of action 1

input parameters

output parameters

Name of action 2

input parameters

output parameters

Name of action k

input parameters

output parameters

…

Service Tree

Client 1 ********

Client 2 ********

…

Client r ********

Clients data

Subtransaction monitor

Service 1

Service Tree

Client 1 ********

Client 2 ********

…

Client r ********

Clients data

Subtransaction monitor

Service s

…

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.

Ontology-Based Transaction Ontology-Based Transaction Management for the Semantic WebManagement 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.

21

Web Resource/Service Integration:Server-Based Transaction Monitor

Server Client

Server

Webresource /

service

Webresource /

service

Transaction Service

TM

wireless

22

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

ServerClient

Server

Webresource /

service

TM

Webresource /

service

wireless

wireless

Public merchants,public customers, publicinformation providers

…

…

Clients

SMOs

SMRs

Maps<path network>

Maps<business points>

Integration,Analysis,Learning

Businessknowledge

Server

I

C

I

I

S

I

Negotiation,Contracting,

Billing

Meta-Profiles

Profiles

XMLWML

LocationProviders

Server

Map ContentProviders

Server

ContentProviders

Server

…

…

…

ExternalEnvironment

XML

$$$ Banks

Architecture for a Mobile P-Commerce ServiceArchitecture 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.

BANK: P-Commerce Service providerBANK: P-Commerce Service provider

Personal ontologyPersonal ontology General ontologyGeneral ontologyAutomatic:Automatic:

Mapping and TransactionsMapping and Transactions

Service UserService User

Service UserService User

Service UserService User Service UserService User

Service UserService User

Service UserService User

via resources and users annotationsvia resources and users annotations

18

Multimeetmobile Project (2000-2001)

Information TechnologyResearch Institute(University of Jyvaskyla):Customer-oriented research anddevelopment in Information Technology

http://www.titu.jyu.fi/eindex.html

Multimeetmobile (MMM) Project(2000-2001):Location-Based Service System and TransactionManagement in Mobile Electronic Commerce

http://www.cs.jyu.fi/~mmm

Academy of FinlandProject (1999):Dynamic Integration ofClassification Algorithms

Mobile Location-Based Mobile Location-Based Service in Semantic WebService in Semantic Web

19

M-Commerce LBS systemhttp://www.cs.jyu.fi/~mmm

In the framework of the Multi Meet Mobile(MMM) project at the University of Jyväskylä,a LBS pilot system, MMM Location-basedService system (MLS), has been developed.MLS is a general LBS system for mobileusers, offering map and navigation acrossmultiple geographically distributed servicesaccompanied with access to location-basedinformation through the map on terminal’sscreen. MLS is based on Java, XML and usesdynamic selection of services for customersbased on their profile and location.

Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,Developing GIS-Supported Location-BasedServices, In: Proceedings of WGIS 2001 - FirstInternational Workshop on Web GeographicalInformation Systems, 3-6 December, 2001, Kyoto,Japan, pp. 423-432.

2 0

A d a p t i v e i n t e r f a c e f o r M L S c l i e n t

O n l y p r e d i c t e d s e r v i c e s , f o r t h e c u s t o m e r w i t h k n o w n p r o f i l ea n d l o c a t i o n , w i l l b e d e l i v e r e d f r o m M L S a n d d i s p l a y e d a tt h e m o b i l e t e r m i n a l s c r e e n a s c l i c k a b l e “ p o i n t s o f i n t e r e s t ”

21

Route-based personalization

Static Perspective Dynamic Perspective 2 2

I n d u c t i v e l e a r n i n g o f c u s t o m e rp r e f e r e n c e s w i t h i n t e g r a t i o n o f p r e d i c t o r s

rrmrr yxxx ,...,, 21

S a m p l e I n s t a n c e s

tmtt xxx ,...,, 21

y t

L e a r n i n g E n v i r o n m e n t

P 1 P 2 . . . P n

P r e d i c t o r s / C l a s s i f i e r s

T e r z i y a n V . , D y n a m i c I n t e g r a t i o n o f V i r t u a l P r e d i c t o r s , I n : L . I . K u n c h e v a , F .S t e i m a n n , C . H a e f k e , M . A l a d j e m , V . N o v a k ( E d s ) , P r o c e e d i n g s o f t h e I n t e r n a t i o n a l I C S CC o n g r e s s o n C o m p u t a t i o n a l I n t e l l i g e n c e : M e t h o d s a n d A p p l i c a t i o n s - C I M A ' 2 0 0 1 , B a n g o r ,W a l e s , U K , J u n e 1 9 - 2 2 , 2 0 0 1 , I C S C A c a d e m i c P r e s s , C a n a d a / T h e N e t h e r l a n d s , p p . 4 6 3 - 4 6 9 .

Machine-to-Machine CommunicationMachine-to-Machine Communication

P2P ontology

P2P ontology

Heterogeneous machines can “understand” each other while exchanging data due to shared ontologies

Semantic Web-Supported Sharing and Semantic Web-Supported Sharing and Integration of Web ServicesIntegration of Web Services

Different companies would be able to share and use cooperatively their Web resources and services due to standardized descriptions of their resources.

P2P ontology

P2P ontology

Corporate/Business HubCorporate/Business Hub

Publish own resource descriptions

Advertise own services

Lookup for resources with semantic searchAutomated access to enterprise (or partners’) resources

Hub ontologyand 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.

Web Services for Smart DevicesWeb 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 ServicesGlobal 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 PlatformsEmbedded 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

OntoServ.NetOntoServ.Net Challenges Challenges

• New group of Web service users – smart smart industrial devicesindustrial devices.

• InternalInternal (embedded) and externalexternal (Web-based) agent enabled service platformsservice platforms.

• “Mobile Service ComponentMobile 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.

Agents in Semantic WebAgents 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

The Challenge: The Challenge: GGlobal lobal UUnderstanding enderstanding eNNvironmentvironment ( (GUNGUN))

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 ConceptGUN 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”

Semantic Web: Before GUNSemantic Web: Before GUN

Semantic Web Resources

Semantic Web Applications

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

resources

GUN Concept:GUN Concept: All GUN resources “understand” each otherAll GUN resources “understand” each other

Real World objects

OntoAdapters

Real World Object ++ OntoAdapter +

+ OntoShell == GUN ResourceGUN Resource

GUNGUN

OntoShells

Real World objects of new generation (OntoAdapter inside)

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.

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 forP2P-networks based on the recently announced JXTA framework.

http://edutella.jxta.org/

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)GUNGUN

environment

Java package

Dynamic Link Library

Database server

cgi-script (semantic) OntoAdapter

Industrial Ontologies GroupIndustrial Ontologies GroupFuture Plans:Future Plans:

“Applications of Wireless Semantic WebApplications of Wireless Semantic Web”

Semantically annotated personal dataSemantically annotated personal data

Virtually all resources have to be marked with semantic labels that show explicitly theVirtually all resources have to be marked with semantic labels that show explicitly themeaning of the resource (piece of data, fact, value etc.) It will make possible for user: 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 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 modelTo access own and other’s resources with semantic queries using “terms” of own model– To be able integrate data from other sources To be able integrate data from other sources

(semantics of data is important, data can be converted/translated if needed and appropriate mapping exists)(semantics of data is important, data can be converted/translated if needed and appropriate mapping exists)

Applications will have:Applications will have:– Possibility to discover and operate with user information and preferencesPossibility to discover and operate with user information and preferences– Possibility to share information with applications at other devices and elsewherePossibility to share information with applications at other devices and elsewhere

My data descriptionmodel (ontology)

Commondata semantic descriptions

(ontologies)

My resourcesand their descriptions

Personaldata-view

Applications

mapping between views

Other people’sdata-views

User data becomes available to variety of applications and other people

Semantic Web Semantic Web Inside™Inside™

Commitmentto ontology

Modelling of personal data viewsModelling of personal data views

Simple user data view (as is in most of mobile phones)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 languagesit will be stored in universal data format.

Building own data model…Building own data model…

added slot (property/field)

inherited slot

Building own data structureBuilding own data structure

added slot (property/field)

inherited slot

Inherited properties

“Relative is a kind of friend”

“Relative is a kind of friend”

Links to otherdata entities

Building own data structureBuilding own data structure

added slot (property/field)

inherited slot

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

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

Using generated interfaceUsing 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

Access your data quickly and easily…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

Customizable personal information Customizable personal information management environmentmanagement environment

Personal data “view”: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: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 possibleIn such environment even development of own applications/scripts can be possible

Ontologies Ontologies andand Semantic Web Semantic Web will enable such kind of applicationswill enable such kind of applications

Easy-to-use, flexible, customizabledata management for users

Repositories of readydata-views

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

Enabled collaboration and interoperability

OntoCacheOntoCache

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.

Personalontology

OntoCacheOntoCache: : benefitsbenefits

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

Phone calls are also possible between mobile terminal agents. They are performed without human participation in order to exchange local information.

Agent-to-Agent communicationAgent-to-Agent communication

Semantic annotation of the local data enables its intelligent processing by software. Ontologies provide interoperability between heterogeneous peers.

Agent-to-Agent communicationAgent-to-Agent communication

Health

Cooking

Business

?Whatever

semantics enablesintelligent data processing

ontological relationsdefine possible

cooperation betweendomain agents

shared ontologyensures

interoperability

TelemedicineTelemedicine

InIn thethe officeofficeOutsideOutside

FishingFishing

AnywhereAnywhere

At universityAt university

On a beachOn a beach

Health CenterHealth Center

Cases of Cases of

“Human “Human

Maintenance” Maintenance”

ActivitiesActivities

InteractionInteraction

“Recovery” Agents

“Diagnostic” Agents

“Platform Steward”

“WatchDog”“Therapist”

Human and Human and Local Health Maintenance CenterLocal Health Maintenance Center

Remote Health Maintenance CenterRemote Health Maintenance Center

“Recovery” Agents

“Diagnostic” Agents

“Therapist”

“Platform Steward”

Maintenance Crew Service

Health Maintenance Health Maintenance without without barriersbarriers

Anytime and AnywhereAnytime and Anywhere

OntoGamesOntoGames:: New Games GenerationNew Games Generation

CGPCGPPUPPUP

Personal User ProfilePersonal User Profile Common Games ProfileCommon Games Profile

Personal ontologyPersonal ontology General ontologyGeneral ontology

OntoGamesOntoGames: Semantic Games Space: Semantic Games Space

Personal ontologyPersonal ontology General ontologyGeneral ontology

OntoGamesOntoGames: Exit in the Real Life: Exit in the Real Life

Realit

y connec

tion

Realit

y connec

tion

via the g

ame

via the g

ame

Reality connection

Reality connection

via the game

via the game

General ontologyGeneral ontologyPersonal ontologyPersonal ontology

Non Stop Game - Non Stop LifeNon Stop Game - Non Stop Life

OntoGamesOntoGamesCCONNECTING ONNECTING P PEOPLEEOPLE

BANKBANK: Data : Data annotationannotation

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 ofsome 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

An

no

tate

d d

ata

(RD

F)

Processing of data by some other semantic-aware applications

BANKBANK: : Customer’s data processingCustomer’s data processing

DataStorage

BankClients

Ontology

BankClients

Input forms

Intelligentontology-based

software

Clients clustering

BANKBANK:: Services annotationServices annotation

Semantics enabled services – Semantics enabled services – easy way to use for customereasy way to use for customer

Semantically Semantically annotated bank annotated bank

servicesservices

I want to …Information filing, Information filing, all documentation all documentation and transactionsand transactions

Less detailed Less detailed informationinformation

Agent-assistant Agent-assistant

Customer Customer

BANKBANK: Loan Borrower annotation: Loan Borrower annotation

Loan borrowersLoan borrowers

Bank - investor Bank - investor

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

borrowers informationborrowers information

Read Our Recent ReportsRead 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: Semantic Web and Peer-to-Peer: Integration and Interoperability in IndustryIntegration and Interoperability in Industry

• Semantic Web Enabled Web Services: Semantic Web Enabled Web Services: State-of-Art and ChallengesState-of-Art and Challenges

• Distributed Mobile Web Services Based on Semantic Web: Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance SystemDistributed Industrial Product Maintenance System

• Available online in: http://www.cs.jyu.fi/ai/OntoGroup/index.html

Industrial Ontologies GroupIndustrial Ontologies Group

V. Terziyan

A. Zharko

O. Kononenko

O. Khriyenko

Semantic Web: The Future starts todaySemantic Web: The Future starts today

e-Business,net-marketse-Business,net-markets

“Web Of Trust”“Web Of Trust”

Enterprise

Application

Integration

Enterprise

Application

Integration

Interoperability standardsInteroperability standards

Web-servicesWeb-services

Industrial Ontologies Group: Examples Industrial Ontologies Group: Examples of Related Contactsof Related Contacts

24

Participation inOntoWeb Network

The goal of the OntoWeb Network is to bringtogether researchers and industrials comingfrom the research and applications areas,promoting interdisciplinary work andstrengthening the European influence onSemantic Web standardisation efforts such asthose based on RDF and XML. Europe'scultural diversity and multi linguality, togetherwith the strong scientific competenciesexisting in the ontology field, may give Europea unique opportunity to fully exploit ontology-based technology and to play a leading role inthese emerging area.

http://www.ontoweb.org

24

University of Jyvaskylais Member of MeT

MeT is an initiative founded byEricsson, Motorola and Nokiato establish a framework forsecure mobile transactions,ensuring a consistent userexperience independent ofdevice, service and network.

http://www.mobiletransaction.org

25

University of Jyvaskylais Member of WIM

http://www.cs.auc.dk/WIMWireless Information Management (WIM) is aresearch training network involvingresearchers from six universities in Denmark,Finland, Lithuania, Norway, and Sweden.Approximately 30 Ph.D. students and theiradvisors and colleagues take part. WirelessInformation Management encompasses themanagement of information obtained fromsensors as well as the management ofinformation involving mobile objects, both ofwhich types of information concern continuouschange, be it in virtual spaces or physicalspace. These types of data will gainprominence in step with the increasingdeployment of wireless communications andsensor technologies.The project aims to offer training in this area,in which there are significant industrialstrengths and interests in the Nordic region.

1. Aalborg University2. Norwegian University of Science and Technology3. University of Jyvaskyla4. Uppsala University5. Vilnius Gediminas Technical University6. Agder University College

26

IT Faculty (Jyvaskyla) - AI Department (Kharkov):Ukrainian students in Mobile Computing Line

Students Background

-C and JAVA Programming

-Network Management

-Mobile Technologies

-Intelligent Agents Technologies

-Web-content Filtering and Personalization

-Data and Web Mining

-Knowledge Management

-Semantic Web (XML, RDF, RDF Schema,DAML)

-Mobile Commerce Applications

-Object-oriented Design

-Database Management

Special personal abilities

-strong motivation-analytical thinking-adaptivity-self-learning-knowledge acquisition-flexibility-professionalism

http://www.cs.jyu.fi/ai/projects/master01/

Distributed Artificial Intelligence inMobile Environment (2 ov.)

Lecturer: Vagan Terziyan

University of Jyvaskyla, MIT Department, Fall 2001, 2002

Vrije Universiteit Amsterdam, AI Department, Fall 2001

Intelligent Web Applications (2 ov.)

Lecturer: Vagan Terziyan

Vrije Universiteit Amsterdam, AI Department, Fall 2002

Web Content Management (6 ov.)

Lecturer: Vagan Terziyan

Jyvaskyla Polytechnic, Spring 2002

University of Jyvaskyla Experience:University of Jyvaskyla Experience:Examples of Related CoursesExamples of Related Courses

18

Digitaalisen median erityiskysymyksiä (2 ov) seminaarin aihepiiri:

Semanttinen webLecturer: Airi Salminen

University of Jyvaskyla, CS & IS Department, Spring 200218

Structured Electronic Documentation

Lecturer: Matthieu Weber

University of Jyvaskyla, MIT Department, Fall 2001, 2002

mweber@mit.jyu.fi

Intelligent Information Integrationin Mobile Environment (4 ov.)

Lecturer: Vagan TerziyanUniversity of Jyvaskyla, MIT Department, Spring 2002

Cooperation with American UniversitiesCooperation with American Universities

Ioannis KakadiarisIoannis Kakadiaris Ass. Professor, Department of Computer Science, University of University of HoustonHouston, 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 CannyJohn CannyProfessor, Division of Computer Science, University of University of CaliforniaCalifornia, , BerkeleyBerkeley, 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.

• 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 WebCompany Benefits from the Semantic Web

ConclusionConclusion

• Semantic Web is not only a technologytechnology as many used to name it;

• Semantic Web is not only an environmentenvironment as many naming it now;

• Semantic WebSemantic Web it is a new contextcontext 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.fihttp://www.cs.jyu.fi/ai/vagan (tel. +358 14 2604618)

“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