1 © Copyright 2010 Dieter Fensel and Mick Kerrigan Intelligent Systems Semantic Web and Services.

1© Copyright 2010 Dieter Fensel and Mick Kerrigan

Intelligent Systems

Semantic Web and Services

2

Where are we?

# Title1 Introduction

2 Propositional Logic

3 Predicate Logic

4 Reasoning

5 Search Methods

6 CommonKADS

7 Problem-Solving Methods

8 Planning

9 Software Agents

10 Rule Learning

11 Inductive Logic Programming

12 Formal Concept Analysis

13 Neural Networks

14 Semantic Web and Services

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Agenda

• Semantic Web - Data• Motivation• Technical Solution: URI, RDF, RDFS, OWL, SPARQL• Illustration by Larger Examples: KIM Browser Plugin, Disco Hyperdata Browser• Extensions: Linked Open Data

• Semantic Web – Processes• Motivation• Technical Solution: Semantic Web Services, WSMO, WSML, SEE, WSMX• Illustration by Larger Examples: SWS Challenge, Virtual Travel Agency, WSMX

at work• Extensions: Mobile Services, Intelligent Cars, Intelligent Electricity Meters

• Conclusions

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4

SEMANTIC WEB - DATA

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MOTIVATION

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• The current Web has its limitations when it comes to:1. finding relevant information2. extracting relevant information3. combining and reusing information

Limitations of the current Web

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• Finding information on the current Web is based on keyword search

• Keyword search has a limited recall and precision due to:– Synonyms:

• e.g. Searching information about “Cars” will ignore Web pages that contain the word “Automobiles” even though the information on these pages could be relevant

– Homonyms:• e.g. Searching information about “Jaguar” will bring up pages containing

information about both “Jaguar” (the car brand) and “Jaguar” (the animal) even though the user is interested only in one of them

Limitations of the current WebFinding relevant information

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• Keyword search has a limited recall and precision due also to:– Spelling variants:

• e.g. “organize” in American English vs. “organise” in British English– Spelling mistakes– Multiple languages

• i.e. information about same topics in published on the Web on different languages (English, German, Italian,…)

• Current search engines provide no means to specify the relation between a resource and a term– e.g. sell / buy

Limitations of the current WebFinding relevant information

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• One-fit-all automatic solution for extracting information from Web pages is not possible due to different formats, different syntaxes

• Even from a single Web page is difficult to extract the relevant information

Limitations of the current WebExtracting relevant information

Which book is about the Web?

What is the priceof the book?

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• Extracting information from current web sites can be done using wrappers

Limitations of the Traditional WebExtracting relevant information

WEBHTML pages

Layout

Structured Data,Databases,

XMLStructure

Wrapper

extractannotatestructure

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• The actual extraction of information from web sites is specified using standards such as XSL Transformation (XSLT) [1]

• Extracted information can be stored as structured data in XML format or databases.

• However, using wrappers do not really scale because the actual extraction of information depends again on the web site format and layout

Limitations of the Traditional WebExtracting relevant information

[1] http://www.w3.org/TR/xslt

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• Tasks often require to combine data on the Web1. Searching for the same information in

different digital libraries2. Information may come from different web

sites and needs to be combined

Limitations of the Traditional WebCombining and reusing information

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Example: I want travel from Innsbruck to Rome.

1. Searches for the same information in different digital libraries

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Example: I want to travel from Innsbruck to Rome where I want to stay in a hotel and visit the city

2. Information may come from different web sites and needs to be combined

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How to improve current Web?

• Increasing automatic linking among data• Increasing recall and precision in search• Increasing automation in data integration• Increasing automation in the service life cycle

• Adding semantics to data and services is the solution!

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TECHNICAL SOLUTIONS

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Uniform Resource Identifier

• Uniform Resource Identifiers (URIs) are used to identify resources, not just things that exists on the Web, e.g. Dieter Fensel, University of Innsbruck

Taken from http://www.w3.org/TR/webarch/

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Resource Description Framework (RDF)

• The Resource Description Framework (RDF) provides a domain independent data model

• Resource (identified by URIs)– Correspond to nodes in a graph– E.g.:

http://www.w3.org/http://example.org/#johnhttp://www.w3.org/1999/02/22-rdf-syntax-ns#Property

• Properties (identified by URIs)– Correspond to labels of edges in a graph– Binary relation between two resources– E.g.:

http://www.example.org/#hasName http://www.w3.org/1999/02/22-rdf-syntax-ns#type

• Literals– Concrete data values– E.g.:

"John Smith", "1", "2006-03-07"

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Resource Description Framework (RDF) – Triple Data Model

• Triple data model: <subject, predicate, object>

– Subject: Resource or blank node– Predicate: Property– Object: Resource, literal or blank node

• Example:<ex:john, ex:father-of, ex:bill>

• Statement (or triple) as a logical formula P(x, y), where the binary predicate P relates the object x to the object y.

• RDF offers only binary predicates (properties)

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Resource Description Framework (RDF) – Graph Model

• The triple data model can be represented as a graph

• Such graph is called in the Artificial Intelligence community a semantic net

• Labeled, directed graphs– Nodes: resources, literals– Labels: properties– Edges: statements

ex:john ex:billex:father-of

ex:tom

ex:father-of

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RDF Schema (RDFS)

• RDF Schema (RDFS) is a language for capturing the semantics of a domain, for example:– In RDF:

<#john, rdf:type, #Student>– What is a “#Student”?

• RDFS is a language for defining RDF types:– Define classes:

• “#Student is a class”– Relationships between classes:

• “#Student is a sub-class of #Person”– Properties of classes:

• “#Person has a property hasName”

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RDF Schema (RDFS)

• Classes:<#Student, rdf:type, #rdfs:Class>

• Class hierarchies:<#Student, rdfs:subClassOf, #Person>

• Properties:<#hasName, rdf:type, rdf:Property>

• Property hierarchies:<#hasMother, rdfs:subPropertyOf, #hasParent>

• Associating properties with classes (a):– “The property #hasName only applies to #Person”

<#hasName, rdfs:domain, #Person>

• Associating properties with classes (b):– “The type of the property #hasName is #xsd:string”

<#hasName, rdfs:range, xsd:string>

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RDF Schema (RDFS) - Example

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Web Ontology Language (OWL)

• RDFS has a number of Limitations:– Only binary relations– Characteristics of Properties, e.g. inverse, transitive, symmetric– Local range restrictions, e.g. for class Person, the property hasName has range

xsd:string– Complex concept descriptions, e.g. Person is defined by Man and Woman– Cardinality restrictions, e.g. a Person may have at most 1 name– Disjointness axioms, e.g. nobody can be both a Man and a Woman

• The Web Ontology Language (OWL) provides an ontology language, that is a more expressive Vocabulary Definition Language for use with RDF

– Class membership– Equivalance of classes– Consistency– Classification

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OWL

• OWL is layered into languages of different expressiveness– OWL Lite: Classification Hierarchies, Simple Constraints– OWL DL: Maximal expressiveness while maintaining tractability– OWL Full: Very high expressiveness, loses tractability, all syntactic freedom of RDF

• More expressive means harder to reason with

• Different Syntaxes:– RDF/XML (Recommended for Serialization)– N3 (Recommended for Human readable Fragments)– Abstract Syntax (Clear Human Readable Syntax)

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lite

DL

Full

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OWL – Example: The Wine Ontology

• An Ontology describing wine domain• One of the most widely used examples for OWL and referenced by

W3C.• There is also a wine agent associated to this ontology that performs

OWL queries using a web-based ontological mark-up language. That is, by combining a logical reasoner with an OWL ontology.

• The agent's operation can be described in three parts: consulting the ontology, performing queries and outputting results.

• Available here: http://www.w3.org/TR/owl-guide/

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OWL – Example: The Wine Ontology Schema

[http://mysite.verizon.net/jflynn12/VisioOWL/VisioOWL.htm]

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SPARQL – Querying RDF

• SPARQL– RDF Query language– Based on RDQL– Uses SQL-like syntax

• Example:PREFIX uni: <http://example.org/uni/>

SELECT ?nameFROM <http://example.org/personal>WHERE { ?s uni:name ?name.?s rdf:type uni:lecturer }

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SPARQL Queries

PREFIX uni: <http://example.org/uni/>SELECT ?nameFROM <http://example.org/personal>WHERE { ?s uni:name ?name. ?s rdf:type uni:lecturer }

• PREFIX– Prefix mechanism for abbreviating URIs

• SELECT– Identifies the variables to be returned in the query answer– SELECT DISTINCT– SELECT REDUCED

• FROM– Name of the graph to be queried– FROM NAMED

• WHERE– Query pattern as a list of triple patterns

• LIMIT• OFFSET• ORDER BY

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SPARQL Example Query 1

“Return the full names of all people in the graph”

PREFIX vCard: <http://www.w3.org/2001/vcard-rdf/3.0#>SELECT ?fullNameWHERE {?x vCard:FN ?fullName}

result:

fullName================="John Smith""Mary Smith"

@prefix ex: <http://example.org/#> .@prefix vcard: <http://www.w3.org/2001/vcard-rdf/3.0#> .ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary .ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29 .

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“Return the relation between John and Mary”

PREFIX ex: <http://example.org/#>SELECT ?pWHERE {ex:john ?p ex:mary}

result:

p=================<http://example.org/#marriedTo>


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“Return the spouse of a person by the name of John Smith”

PREFIX vCard: <http://www.w3.org/2001/vcard-rdf/3.0#>PREFIX ex: <http://example.org/#>SELECT ?yWHERE {?x vCard:FN "John Smith".

?x ex:marriedTo ?y}result:

y=================<http://example.org/#mary>


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ILLUSTRATION BY LARGER EXAMPLES

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Annotated Content

• KIM Browser PluginWeb content is annotated using ontologiesContent can be searched and browsed intelligently

Select one or more concepts from the ontology…… send the currently loaded web page to the Annotation Server

Illustration 1 – KIM Browser Plugin

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Dereferencable URI

Disco Hyperdata Browser navigating the Semantic Web as an unbound set of data sources

Illustration 2 – Disco Hyperdata Browser

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EXTENSIONS

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Extensions: Linked Open Data

• Linked Data is a method for exposing and sharing connected data via dereferenceable URI’s on the Web

– Use URIs to identify things that you expose to the Web as resources– Use HTTP URIs so that people can locate and look up (dereference) these things– Provide useful information about the resource when its URI is dereferenced– Include links to other, related URIs in the exposed data as a means of improving

information discovery on the Web

• Linked Open Data is an initiative to interlink open data sources– Open: Publicly available data sets that are accessible to everyone– Interlinked: Datasets have references to one another allowing them to be used

together

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Extensions: Linked Open Data

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Extensions: Linked Open Data - FOAF

• Friend Of A Friend (FOAF) provides a way to create machine-readable pages about:

– People– The links between them– The things they do and create

• Anyone can publish a FOAF file on the web about themselves and this data becomes part of the Web of Data

• FOAF is connected to many other data sets, including– Data sets describing music and musicians (Audio Scrobbler, MusicBrainz)– Data sets describing photographs and who took them (Flickr)– Data sets describing places and their relationship (GeoNames)

<foaf:Person><foaf:name>Dieter Fensel</foaf:name><foaf:homepage rdf:resource="http://www.fensel.com"/>

</foaf:Person>

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Extensions: Linked Open Data - GeoNames

• The GeoNames Ontology makes it possible to add geospatial semantic information to the Web of Data

• We can utilize GeoNames location within the FOAF profile

• GeoNames is also linked to more datasets– US Census Data– Movie Database (Linked MDB)– Extracted data from Wikipedia (DBpedia)

<foaf:Person><foaf:name>Dieter Fensel</foaf:name><foaf:homepage rdf:resource="http://www.fensel.com"/><foaf:based_near ” http://ws.geonames.org/rdf?

geonameId=2775220"/></foaf:Person>

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Extensions: Linked Open Data - DBpedia

• DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web

• As our FOAF profile has been linked to GeoNames, and GeoNames is linked to DBpedia, we can ask some interesting queries over the Web of Data

– What is the population of the city in which Dieter Fensel lives?=> 117916 people

– At which elevation does Dieter Fensel live?=> 574m

– Who is the mayor of the city in which Dieter Fensel lives=> Hilde Zach

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SEMANTIC WEB - PROCESSES

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MOTIVATION

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Motivation

http://www.sti-innsbruck.at/dip-movie

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Motivation

• The Web is moving from static data to dynamic functionality– Web services: a piece of software available over the

Internet, using standardized XML messaging systems – Mashups: The compounding of two or more pieces of

web functionality to create powerful web applications

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Motivation

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Limitations of the current Web Processes

• Web services and mashups are limited by their syntactic nature

• As the amount of services on the Web increases it will be harder to find Web services in order to use them in mashups

• The current amount of human effort required to build applications is not sustainable at a Web scale

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What is needed?

• Formal, machine processable descriptions of processes on the Web that allows easy integration, configuration and reuse

• Semantic support for finding, composing and executing these processes and all the other related tasks

Solution: Combine Semantics and Web processes/services that enables the automation of many of the currently human intensive tasks around Web processes/services

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TECHNICAL SOLUTIONS

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Semantic Web Services

• Brings the benefits of Semantics to the executable part of the Web– Ontologies as data model– Unambiguous definition of service functionality and external interface

• Reduce human effort in integrating services in SOA– Many tasks in the process of using Web services can be automated

• Improve dynamism– New services available for use as they appear– Service Producers and Consumers don’t need to know of each others existence

• Improve stability– Service interfaces are not tightly integrated so even less impact from changes– Services can be easily replaced if they are no longer available– Failover possibilities are limited only by the number of available services

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Semantic Web Services

• Semantic Web Services are a layer on top of existing Web service technologies and do not aim to replace them

• Provide a formal description of services, while still being compliant with existing and emerging technologies

• Distinguish between a Web service (computational entity) and a service (value provided by invocation)

• Make Web services easier to:– Find– Compare– Compose– Invoke

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Conceptual Model for SWS

Formal Language for WSMO

Ontology & Rule Language for the Semantic Web

Technical Overview

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Execution Environment

For SWS

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Strict Decouplingof Modeling Elements

Centrality ofMediation

Ontological Role Separation

Description versus Implementation

WSMOOntology-Based

Web Service versus Service

WSMO – Design Principles

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WSMO – Conceptual Model

Objectives that a client wants toachieve by using Web Services

Formally specified terminology used by all other components

Semantic descriptionof Web Services• Capability (functional)• Interfaces (usage)

Connectors between components with mediation facilities for handling heterogeneities

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WSML – Language Family

WSML - Core

WSML - DL

WSML - Full

WSML - Flight

WSML - Rule

f

with

without

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Exp

ress

ivity

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Semantic Execution Environment

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Semantic Execution Environment

Discovery Ranking Selection

Composition Data Mediation Process Mediation

Process Execution

Lifting & Lowering

base

brokerverticalM

onito

ring

Reasoning Storage

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Semantic Execution Environment - WSMX

WSMX

System

Interface

WSMX ManagerWSMX Manager Core

Administration Framework Interface

Data and C

omm

unication Protocols A

daptersA

dapter 1A

dapter 2A

dapter n... Grounding

CM Wrapper

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SelectorWrapper

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DMWrapper

DataMediator

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PMWrapper

ProcessMediator

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ChoreographyWrapper

Choreography

Interface

Reasoner Interface

ReasonerResource Manager Interface

WSMO Objects Non WSMO Objects

WSMT – Web Services Modelling Toolkit

Service Providers

Web Service 1

Web Service 2

Web Service p

...

Service Requesters

Back-End Application

Agent acting on behalf of service

requester

WSML EditorWSMX Monitor Choreography EditorWSMX Managment Mediator Editor

ComponentWrapper

New Component

Interface

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ILLUSTRATION BY LARGER EXAMPLES

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• Blue company has discovered Moon company on the Web • Blue company wishes to communicate with Moon company

• Broker required to resolve data and process interoperability issues

Purchase Order

Purchase Order Confirmation

id

cid

openOrder

addItem*

closeOrder

Blue Company can only send POs and

receive PO Confirmations

Allows the opening of a PO, the specification of the items

to be purchased and the closing of the PO

Receives a customer id and returns a full

customer descriptionIllustration 1: SWS Challenge

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Rail Services

Flight Services

Hotel Services

Car Hire Services

Illustration 2: Virtual Travel Agency

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Illustration 3: WSMX At Work

WSMX

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Chor. wrapperpicks up event for Chor. component

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RMWrapper

Resource Manager

Interface

ParserWrapper

Parser

Interface

DiscoveryWrapper

Discovery

Interface

SelectorWrapper

Selector

Interface

DMWrapper

DataMediator

Interface

PMWrapper

ProcessMediator

Interface

ChoreographyWrapper

Choreography

Interface

Reasoner Interface




Service Providers

Web Service 1

Web Service 2

Web Service p

...

Service Requesters



requester


ComponentWrapper

New Component

Interface

Set of Web Servicedescriptionsexpressed in WSMLsent to adapter.

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WSMX

System

Interface



Data and C

omm


daptersA

dapter 1A

dapter 2A


CM Wrapper


Interface

Invoker Receiver

RMWrapper

Resource Manager

Interface

ParserWrapper

Parser

Interface

DiscoveryWrapper

Discovery

Interface

SelectorWrapper

Selector

Interface

DMWrapper

DataMediator

Interface

PMWrapper

ProcessMediator

Interface

ChoreographyWrapper

Choreography

Interface

Reasoner Interface




Service Providers

Web Service 1

Web Service 2

Web Service p

...

Service Requesters



requester


ComponentWrapper

New Component

Interface

Set of Web Servicedescriptions expressedin requester’s ownformat returned togoal requester.

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EXTENSIONS

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Extensions: Mobile Services

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Extensions: Mobile Services

• Extending the mobile and sensors networks with Semantic technologies, Semantic Web will enable:

– Interoperability at the level of sensors data and protocols– More precise search for mobile capabilities and sensors with desired capability

From: http://www.opengeospatial.org/projects/groups/sensorweb

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Extensions: Intelligent Cars

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Extensions: Intelligent Cars

• A key aspect of future cars will be intelligence

– Real-time data collection– Car-to-X communication– Interaction Systems– Driver Assistance

• Semantic Technologies can be used to add this intelligence through:

– Machine awareness of context (Spatio-temporal concerns, mood, etc..)

– Management of large volumes of real-time data

– Enabling interoperability between systemsFrom: http://www.nff.tu-bs.de/index.php?

id=464&L=1

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Extensions: A Smarter Electricity Grid

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Extensions: A Smarter Electricity Grid

• Managing increasingly dispersed energy resources is crucial to ensure efficient electricity consumption, for example

– Wind farms– Solar panels

• Grid operators cannot be sure at any given time:– Which electricity generators are connected– If they are operational or not– How much power they are outputting

• Semantic technologies can be used to enable “self-describing networks”– Each member of the network autonomously publishes semantic data about itself– Enables more efficient automated grid management technologies

• Ultimately semantic technologies can provide a “Smart Electricity Grid”

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SUMMARY

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Summary

• The Semantic Web provides a mechanism for – Representing knowledge on the Web– Annotating data on the Web– Annotating services on the Web

• Everything is identified by a URI• RDF to assert relations between resources• RDFS and OWL to make statements about types• SPARQL to query RDF data• WSMO as a conceptual model • WSML for describing services at different levels of expressivity• WSMX as a Semantic Execution Environment for bringing requesters

and providers together

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REFERENCES

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References

• Mandatory reading:– T. Berners-Lee, J. Hendler, O. Lassila. The Semantic Web, Scientific American, 2001.– Dieter Fensel, Holger Lausen, Axel Polleres, Jos de Bruijn, Michael Stollberg, Dumitru

Roman, John Domingue, Enabling Semantic Web Services: The Web Service Modeling Ontology, Springer-Verlag, 2007

• Further reading:– Dieter Fensel, Mick Kerrigan, Michal Zaremba (Eds.), Implementing Semantic Web Services:

The SESA Framework. Springer-Verlag, 2008.– Jos de Bruijn, Dieter Fensel, Mick Kerrigan, Uwe Keller, Holger Lausen, and James Scicluna:

Modeling Semantic Web Services, Springer-Verlag, 2008– D. Fensel. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce,

2nd Edition, Springer 2003.– G. Antoniou and F. van Harmelen. A Semantic Web Primer, (2nd edition), The MIT Press

2008. – H. Stuckenschmidt and F. van Harmelen. Information Sharing on the Semantic Web, Springer

2004. – T. Berners-Lee. Weaving the Web, HarperCollins 2000– T.R. Gruber, Toward principles for the design of ontologies used or knowledge sharing? , Int.

J. Hum.-Comput. Stud., vol. 43, no. 5-6,1995

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References

– David Martin, et al., OWL-S: Semantic Markup for Web Services, W3C Member Submission 22 November 2004, http://www.w3.org/Submission/OWL-S.

– Joel Farrell and Holger Lausen, Semantic Annotations for WSDL and XML Schema, W3C Recommendation 28 August 2007, http://www.w3.org/TR/sawsdl

– Rama Akkiraju et al., Web Service Semantics - WSDL-S, W3C Member Submission 7 November 2005, http://www.w3.org/Submission/WSDL-S

– Steve Battle et al., Semantic Web Services Framework (SWSF), W3C Member Submission 9 September 2005, http://www.w3.org/Submission/SWSF

– Semantic Web Primer: http://www.ics.forth.gr/isl/swprimer/– RDF Primer: http://www.w3.org/TR/REC-rdf-syntax/ – RDF Schema: http://www.w3.org/TR/rdf-schema/– OWL Guide: http://www.w3.org/TR/owl-guide/– RDF SPARQL Protocol: http://www.w3.org/TR/rdf-sparql-protocol/– Linked Open Data: http://linkeddata.org/– Linked Open Data Tutorial: http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/– WSMO: http://www.wsmo.org/TR/d2/– WSML: http://www.wsmo.org/TR/d16/d16.1/– WSMO/WSML Tutorials: http://wiki.sti2.at/index.php?title=WSMT_Tutorials

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References

• Wikipedia links:– URI: http://en.wikipedia.org/wiki/URI– RDF; http://en.wikipedia.org/wiki/Resource_Description_Framework– RDFS: http://en.wikipedia.org/wiki/RDFS– SPARQL: http://en.wikipedia.org/wiki/SPARQL– WSMO: http://en.wikipedia.org/wiki/WSMO– WSML: http://en.wikipedia.org/wiki/Web_Services_Modeling_Language

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Exam Preparation

• Exam will take place on the DD.MM.YYYY, HH:MM in room X.• Exam time is 90 minutes• The material for exam are the slide sets:

TitleIntroduction

Propositional Logic

Predicate Logic

Theorem Proving, Description Logics and Logic Programming

Search Methods

CommonKADS

Problem Solving Methods

Planning

Agents

Rule Learning

Inductive Logic Programming

Semantic Web and Exam Preparation

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Questions?

1 © Copyright 2010 Dieter Fensel and Mick Kerrigan Intelligent Systems Semantic Web and Services.

Documents

Transcript of 1 © Copyright 2010 Dieter Fensel and Mick Kerrigan Intelligent Systems Semantic Web and Services.