1. ©Ferenc Vajda 1 Semantic Grid Ferenc Vajda vajda@sztaki.hu Computer and Automation Research Institute Hungarian Academy of Sciences

2. ©Ferenc Vajda 2 Data/Information/Knowledge Data: observed facts Information: organized and related facts with attributed properties Knowledge: “sum of what is known”: concepts, objects with characteristics, principles, laws, know-how, etc. Semantics: a term used for meaning, interpretation, knowledge through reasoning

3. ©Ferenc Vajda 3 Different Evaluations of the Grid 1. Grid generations To link supercomputer centers (e.g. I-way) Toolkit- and middleware-based (e.g. Globus) Service-oriented (OGSA)

4. ©Ferenc Vajda 4 Different Evaluations of the Grid 2. 2. Based on the technologies used Protocol-based Service-based Semantic Web based 3.Based on application requirements Data/computational Grid Information Grid Knowledge Grid

5. ©Ferenc Vajda 5 Problems Related to Semantic Web Knowledge Evaluation Knowledge Representation Ontologies Agents

6. ©Ferenc Vajda 6 Resource Description Framework (RDF) -Set of triplets: subject, property,object Metadata: structured data about data Resource identification: Universal Resource Identifier (URI) Most common type of URI: Uniform Resource Locator (URL) Qualified URI: URI + fragment identifier Concepts: -Graph model

7. ©Ferenc Vajda 7 RDF 2. SubjectObject Property -Data types: based on XML Schema -Vocabulary: URI-based (Both nodes and arcs)

8. ©Ferenc Vajda 8 RDF 3.

9. ©Ferenc Vajda 9 What is an Ontology? Greek: ontos = being, logos = science world view regarding a domain shared understanding definitions, inter-relationship conceptualization

10. ©Ferenc Vajda 10 What does an Ontology look like? vocabulary of terms specification of their meaning (i.e. definitions) - highly informal (natural language) - semi-informal (restricted, structured form of natural language) - semi-formal (artificial, formally defined language) - rigorously formal (formal semantics, proofs, completeness)

11. ©Ferenc Vajda 11 Use of Ontologies communication (between people and organizations) system engineering (specifications, reusable components) inter-operability (between systems)

12. ©Ferenc Vajda 12 Ontologies Web Ontology Language (OWL) Ontology: defines the terms used to describe and represent an area of knowledge -taxonomy: object classification + relationship among them (properties and inheritance of properties) -inference rules DAML (DARPA = Defense Advanced Project Agency Agent Markup Language)

13. ©Ferenc Vajda 13 Agents Agent: Capability to understand and integrate diverse information resources (based on domain ontologies)

14. ©Ferenc Vajda 14 Agents 2.

15. ©Ferenc Vajda 15 Semantic Web Layers Credit to Berners-Lee (XML2000 address)

16. ©Ferenc Vajda 16 Semantic Grid

17. ©Ferenc Vajda 17 Semantic Grid Basis: Metadata enabled Goal: Grid + Semantic Web Ontologically principled New e-Science infrastructure

18. ©Ferenc Vajda 18 Services e.g. -semantic database integration -semantic workflow description Base services -data/computational services (network access, resource allocation and scheduling, data shipping, etc.) -information services (query processing, event notification, instrumentation management, etc.) Semantic services

19. ©Ferenc Vajda 19 Services 2. -application Knowledge services -acquisition -modeling -publishing, use and maintenance -resource management

20. ©Ferenc Vajda 20 Knowledge Grid Architecture Credit to Carole Goble et al.

21. ©Ferenc Vajda 21 Roles of Ontologies Credit to Carole Goble et al.

22. ©Ferenc Vajda 22 The term ‘ procedure ’ used by one tool is translated into the term ‘ method ‘ used by the other via the ontology, whose term for the same underlying concept is ‘ process ’. procedure viewer translator Ontology method library give me the procedure for… translator here is the METHOD for… procedure = ??? procedure = process give me the process for… here is the process for… METHOD = process ??? = process Roles of Ontologies (Example) Credit to Rokhlenko Oleg

23. ©Ferenc Vajda 23 Knowledge Services Credit to Carole Goble et al.

24. ©Ferenc Vajda 24 Typical Applications Service discovery Knowledge annotation Workflow composition Data interpretation Collaborative science

25. ©Ferenc Vajda 25 Grid Service Discovery Simple discovery attribute-base name lookup type matching Semantic discovery matchmaking based on ontology description

26. ©Ferenc Vajda 26 Brokering vs. Matchmaking

27. ©Ferenc Vajda 27 Grid Service Discovery Framework Ontology based description used by service provider service requester service matchmaker service registry database Matchmaking process comparison: request to registry decision: based on filters information

28. ©Ferenc Vajda 28 Service Description “What the service does”: service profile “How it works”: ServiceModel “How it is used”: ServiceGrounding Description by RDF(S): Resource Description Framework Schema Service profile description (human readable) functionalities functional attributes

29. ©Ferenc Vajda 29 Service Description 2. Credit to DAML-S White Paper

30. ©Ferenc Vajda 30 Filtering Independent filtering is based on context matching syntactic matching - comparison of profiles - similarity matching - signature matching semantic matching

31. ©Ferenc Vajda 31 myGrid project

32. ©Ferenc Vajda 32 Role of Ontologies in myGrid