1. Semantic Web: The Future Starts Today
Industrial Ontologies Group
Semantic Web: The Future Starts Today
“Industrial Ontologies” Group
Agora Center, University of Jyväskylä, 23 May 2003

2. “Industrial Ontologies” Group: Our History
– We took part in development of the first in USSR Industrial Natural Language Processing System “DESTA”, which included semantic analysis and ontologies;
We took part in development of the first in USSR Industrial Automated Natural Language Programming System “ALISA”, which Enabled Semantic Annotation, Discovery and Integration of software components (prototype of today's Semantic Web Services concept);

3. “Industrial Ontologies” Group: Our History
– 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;
– various projects with industrial partners, e.g. MetaAtom – “Semantic Diagnostics of Ukrainian Nuclear Power Stations based on Metaknowledge”; MetaHuman – industrial medical diagnostics expert system based on Metaknowledge”; Jeweler – metamodelling and control of industrial processes, etc.; got several research grants from Finnish Academy;

4. “Industrial Ontologies” Group: Our History
– 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.
– we took part in MultiMeetMobile Tekes Project, in InBCT Tekes Project in Tempus EU Compact Project in (or in cooperation with) University of Jyvaskyla where we further promote Semantic Web concepts.

5. Industrial Ontologies Group: Important Objective
For us there are no doubts about the possibilities, which Semantic Web opens for industry.
that is why one important objective of our activities is to study appropriate industrial cases, collect arguments, launch industrial projects and develop prototypes for the industrial companies to not only believe together with us but also benefit from the Semantic Web.

6. Why and Where Semantic Web ?
more then 3,000,000,000 web-pages
“Information” burst
ICT needs comprehensive resource management technology
WWW
Needs for integration of businesses
Web Services for e-Business
Standardization and Interoperability problems
Business
Knowledge Management
Consolidate and reuse experience
Standardize knowledge sharing technology
Needs for the intelligent tools to use human’s knowledge

7. Approach: Semantic Web
“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes,
but for automation, integration and reuse
of data across various applications”
The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.

8. Word-Wide Correlated Activities
Semantic Web
Agentcities is a global, collaborative effort
to construct an open network of on-line systems
hosting diverse agent based services.
Semantic Web is an extension of the current
web in which information is given well-defined
meaning, better enabling computers and people
to work in cooperation
Agentcities
Grid Computing
Wide-area distributed computing, or "grid” technologies,
provide the foundation to a number of large-scale efforts
utilizing the global Internet to build distributed computing
and communications infrastructures.
FIPA
FIPA is a non-profit organisation aimed
at producing standards for the interoperation
of heterogeneous software agents.
Web Services
WWW is more and more used for application to application communication.
The programmatic interfaces made available are referred to as Web services.
The goal of the Web Services Activity is to develop a set of
technologies in order to bring Web services to their full potential

9. Semantic Web: New “Users”
applications
agents

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

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

12. Ontologies: the foundation of Semantic Web
Ontologies are key enabling technology for the Semantic Web
“..explicit specification of conceptualization..”
Ontology is formal and rich way to provide shared and common understanding of a domain, that can be used by people and machines
comment
__Thing__
Author
public
private
Access Rights
is-a
Location
Related to
Document
name
Report
is-a
is-a
Web-page
uri
Subject
Instance-of
Instance-of
O. Kononenko
V. Terziyan
public
Author
Access rights
Author
#doc1
#doc2
name
Related to
Semantic Web
Location
uri
Subject
comment
\\AgServ\vagan\InBCT_1.doc
comment
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 S?

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

14. Co-operative Work in Web
WWW

15. Co-operative Work in Semantic Web
WWW

16. Semantic Web is not Only ...

17. … but Also ...

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

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

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

21. The conceptual scheme of the ontology-based transaction management with multiple e-services
Terziyan V., Ontological Modelling of E-Services to Ensure Appropriate Mobile Transactions, In: International Journal of Intelligent Systems in Accounting, Finance and Management, J. Wiley & Sons, Vol. 12, 2003, 14 pp.

22. Ontology-Based Transaction Management for the Semantic Web
Consider two basic transaction management architectures in mobile environment depending on where the Transaction Monitor (TM) will be located. First one (Server-Based) assumes that TM will be located in server side, e.g. within some transaction management service. Second one (Client-Based) supposes that TM is located in mobile client terminal.
The first objective will be to provide and study an integrated mobile transaction management architecture for the Semantic Web applications, which will combine the best features from these two architectures by intelligent switching from one architecture to another one depending on current application context.
There is already some ontological support for Semantic Web resources and services interoperability based on OWL, DAML-S. However to be able to manage transactions in Semantic Web across multiple resources (or services) there will not be enough only ontologies for semantic annotations of these resources; there will be evident need of the ontology for the Semantic Web transactions itself.
The second objective will be developing pilot ontology for the RDF-based semantic annotation of mobile transactions in the Semantic Web.

23. Architecture for a Mobile P-Commerce Service
Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.

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

25. Mobile Location-Based Service in Semantic Web

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

27. Semantic Web-Supported Sharing and Integration of Web Services
Different companies would be able to share and use cooperatively their Web resources and services due to standardized descriptions of their resources.
P2P ontology
P2P ontology

28. Corporate/Business Hub
Hub ontology
and shared domain ontologies
Partners / Businesses
Companies would be able to create “Corporate Hubs”, which would be an excellent cooperative business environment for their applications.
What parties can do:
What parties achieve:
Publish own resource descriptions
2 cases:
Integration within enterprise (corporation)
Integration between separate businesses
Everything remains true for both cases, only terms are changed
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

29. Web Services for Smart Devices
Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc.
"OntoServ.Net"
OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,

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

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

32. OntoServ.Net Challenges
New group of Web service users – smart industrial devices.
Internal (embedded) and external (Web-based) agent enabled service platforms.
“Mobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side.
Semantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.

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

34. Semantically annotated personal data
Virtually all resources have to be marked with semantic labels that show explicitly the
meaning of the resource (piece of data, fact, value etc.) It will make possible for user:
To organize own view on data and use it for data management
To access own and other’s resources with semantic queries using “terms” of own model
To be able integrate data from other sources
(semantics of data is important, data can be converted/translated if needed and appropriate mapping exists)
Applications will have:
Possibility to discover and operate with user information and preferences
Possibility to share information with applications at other devices and elsewhere
My data description model (ontology)
Common
data semantic descriptions (ontologies)
mapping between views
Personal data-view
Semantic Web Inside™
Commitment to ontology
Get ontology of information resources you want to manage as starting point (Design yourself or just download)
Extend ontology as you want using visual tools and wizards
Use dynamically generated from ontological description user interface (forms, controls etc.) that provides all means for data access.
Applications
User data becomes available to variety of
applications and other people
My resources and their descriptions
Other people’s data-views

35. Modelling of personal data views
Simple user data view (as is in most of mobile phones)
Data to store in every instance of defined information model
Actually, this model is a simple ontology of “Personal Data” domain.
Using developed standard ontology languages it will be stored in universal data format.
Model of user’s data and other resources:
Contacts (phone numbers, names etc.)
Notes (some pieces of text)
Calendar (with some events assigned)
It is rather simple, but a good beginning for own data model
creation…..

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

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

38. 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

39. Using generated interface
For described data model forms are generated
Data view is described as an ontology which contains all needed information about data structure. User interface is built dynamically from ontology:
Fields for data
Form layout, types of controls (e.g. picture, checkboxes etc.)
Rules for data that can check some constraints, invoke actions, perform calculations – whatever!

40. Access your data quickly and easily…
Possibilities to build flexible, easily customizable data management applications are great.
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

41. Customizable personal information management environment
Easy-to-use, flexible, customizable
data management for users
Personal data “view”:
Development of own view on personal data
Reusing of existing views (join, modify, extend)
Links between personal and some “global” ontology
Sharing of data:
Applications use data and do it correctly (because of semantics assigned)
Applications can exchange data with external sources
Data can be translated in respect of its semantics (for localization, between different data views, to fit some requirements etc.)
In such environment even development of own applications/scripts can be possible
Ontologies and Semantic Web will enable such kind of applications
Repositories of ready data-views
Enabled collaboration and interoperability
Note: Protégé-2000 ontology development and knowledge acquisition tool was used for demonstration

42. OntoCache Ubiquitous Semantic Web Translation
General ontology
Ubiquitous
Semantic Web
Translation
Ubiquitous computing seems to penetrate into all human environments. Amount of information resources increases significantly that makes search of appropriate data extremely difficult. This stands even worse for mobile devices which have limited computing resources. Semantic annotation of all such information resources in human environments enhance much the efficiency of their search.
Ontocache is a software, capable to process semantically annotated information Web-resources. It provides a local Semantic-based browser of web-resources making their search much easier. Its logic is based on local ontology of the resources the user is interested in. The ontology is used for specification of semantic queries that are sent by software to the external environment. The interface of the browser can combine tree-based navigation with semi-natural queries. Initially some statistically general ontology is set as default in the mobile terminal. However, user can alter it according to his needs (student, doctor, psychologist, etc.).
If the local ontology isn’t detailed enough for composition of a final query its deeper parts are downloaded from external stores. Thus, depending on user preferences some parts of local ontology with time die and some extend. Shared ontologies make the queries and resources descriptions be based on common vocabularies.
Semantic annotations of Web-services (or any other resources) based on shared ontologies enhance much the efficiency of their search/browsing from the PDA. Local ontology adapts permanently to the user preferences.
Personal
ontology

43. OntoCache: benefits
Context and preferences-based adaptation
Support for semi-natural queries
Effective filtering of wide variety of Web-resources
Technology that supports future Ubiquitous Semantic Web
OntoCache is a tool that conforms with future vision of having all information resources semantically annotated. The benefits it gives correspond to the main promise of Semantic Web technology to provide effective filtering in conditions of information burst. The idea takes into account limited computing resources of mobile devices and assumes adaptation of local ontology to the user preferences. Caching of ontology allows decreasing the data traffic in the form of external parts of ontologies.
Semantic data facilitates much more complicated queries than keyword-based ones – semi-natural.
Additionally, the behaviour of the navigator can be context-dependent (location, weather, daylight personalization, etc.)

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

45. Agent-to-Agent communication
semantics enables
intelligent data processing
ontological relations
Business
define possible
Cooking
cooperation between
domain agents
shared ontology
Health
ensures
interoperability
Whatever ?
Semantic enriching mobile device data allows software agents perform its intelligent processing substituting human. Software agents which had been programmed basing on certain ontology can perform domain-specific data processing. Relations between domain ontologies define possible cooperation patterns between correspondent agents. Shared ontology ensures interoperability between agents that exchange information.
annotate problem domains
into related ontologies
programm software
basing on the ontologies
semantically enrich
data basing on ontologies

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

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

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

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

50. BANK: Data annotation
In order to make miscellaneous data gathered and used later for some processing,
every piece of data needs label assigned, which will denote its semantics in terms of
some ontology. Software that is developed with support of that ontology can recognize the data and process it correctly in respect to its semantics.
Annotated data (RDF)
Ontology of gathered data
Web forms and dialogs generated
Processing of data by some other semantic-aware applications

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

52. BANK: Services annotation
Semantically annotated bank services
I want to …
Information filing, all documentation and transactions
Less detailed information
Agent-assistant
Customer
Semantics enabled services – easy way to use for customer

53. BANK: Loan Borrower annotation
Bank - investor
Loan borrowers
Automated support of:
making decisions about trusting
prediction of future trends
via semantically annotated loan borrowers information

54. 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)
Semantic Web and Peer-to-Peer: Integration and Interoperability in Industry
Semantic Web Enabled Web Services: State-of-Art and Challenges
Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance System
Available online in:
V. Terziyan
A. Zharko
O. Kononenko
O. Khriyenko
Industrial Ontologies Group

55. Semantic Web: The Future starts today
Interoperability standards
e-Business,
net-markets
Enterprise Application
Integration
Web-services
“Web Of Trust”

56. Industrial Ontologies Group: Examples of Related Contacts

57. University of Jyvaskyla Experience: Examples of Related Courses

58. Cooperation with American Universities
John Canny
Professor,
Division of Computer Science, University of California, Berkeley, USA
Ioannis Kakadiaris Ass. Professor, Department of Computer Science, University of Houston, USA
Ioannis is the founder and Director of Visual Computing Laboratory and the Director of the Division of Bioimaging and Biocomputation at the UH Institute for Digital Informatics and Analysis. He is the recipient of a year 2000 NSF Early Career Development Award.
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 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).
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.

59. Conclusion
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 )