©Ferenc Vajda
1
Semantic Grid
Ferenc Vajda
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