©ferenc vajda 1 semantic grid ferenc vajda vajda@sztaki.hu computer and automation research...

©Ferenc Vajda

1

Semantic Grid

Ferenc Vajda

vajda@sztaki.hu

Computer and Automation Research Institute

Hungarian Academy of Sciences

©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

©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)

©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

©Ferenc Vajda

5

Problems Related to Semantic Web

• Knowledge Evaluation

• Knowledge Representation

• Ontologies

• Agents

©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

©Ferenc Vajda

7

RDF 2.

Subject ObjectProperty

-Data types: based on XML Schema

-Vocabulary: URI-based (Both nodes and arcs)

©Ferenc Vajda

8

RDF 3.

©Ferenc Vajda

9

What is an Ontology?

Greek: ontos = being, logos = science

• world view regarding a domain

• shared understanding

• definitions, inter-relationship

• conceptualization

©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)

©Ferenc Vajda

11

Use of Ontologies

• communication (between people and organizations)

• system engineering (specifications, reusable components)

• inter-operability (between systems)

©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)

©Ferenc Vajda

13

Agents

Agent: Capability to understand and integrate diverse information resources (based on domain ontologies)

©Ferenc Vajda

14

Agents 2.

©Ferenc Vajda

15

Semantic Web Layers

Credit to Berners-Lee (XML2000 address)

©Ferenc Vajda

16

Semantic Grid

©Ferenc Vajda

17

Semantic Grid

Basis:

• Metadata enabled

Goal:

Grid + Semantic Web

• Ontologically principled

New e-Science infrastructure

©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 (queryprocessing, event notification, instrumentation management,

etc.)

• Semantic services

©Ferenc Vajda

19

Services 2.

-application

• Knowledge services

-acquisition

-modeling

-publishing, use and maintenance

-resource management

©Ferenc Vajda

20

Knowledge Grid Architecture

Credit to Carole Goble et al.

©Ferenc Vajda

21

Roles of Ontologies

Credit to Carole Goble et al.

©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

©Ferenc Vajda

23

Knowledge Services

Credit to Carole Goble et al.

©Ferenc Vajda

24

Typical Applications

• Service discovery

• Knowledge annotation

• Workflow composition

• Data interpretation

• Collaborative science

©Ferenc Vajda

25

Grid Service Discovery

Simple discovery

• attribute-base

• name lookup

• type matching

Semantic discovery

• matchmaking

• based on ontology description

©Ferenc Vajda

26

Brokering vs. Matchmaking

©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

©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

©Ferenc Vajda

29

Service Description 2.

Credit to DAML-S White Paper

©Ferenc Vajda

30

Filtering

Independent filtering is based on

• context matching

• syntactic matching

- comparison of profiles

- similarity matching

- signature matching

• semantic matching

©Ferenc Vajda

31

myGrid project

©Ferenc Vajda

32

Role of Ontologies in myGrid