The Semantic Web: Web of (integrated) Data

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The Semantic Web:

Web of (integrated) Data

Frank van Harmelen Vrije Universiteit Amsterdam

Take home message

Semantic Web = Web of Data

(no longer only web of text, web of pictures)

Set of open, stable W3C standards

Rapidly emerging tools & vendors

Use cases:

z data integration z web services

z knowledge management z search (intranets)

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Outline

The vision

What is required

Machine representation

z

XML, RDF, OWL

Where are we now?

Examples

Things we would

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“Intelligent” things

we can’t do today

Search engines

• concepts, not keywords

• semantic narrowing/widening of queries

Shopbots

• semantic interchange, not screenscraping

E-commerce

z Negotiation, catalogue mapping, personalisation

Web Services

z Need semantic characterisations to find them, z to combine them

Navigation

• by semantic proximity, not hardwired links

...

harmelen harmelen

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Other use-case are

z personalisation z semantic linking z data integration z web services z ...

Sounds good, so..

how is this tackled?

(5)

Outline

The vision

What is required

Machine representation

z

XML, RDF, OWL

Where are we now?

Examples

machine accessible meaning

(What it’s like to be a machine)

disease name symptoms drug administration

Meta-data !

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What is meta-data?

it's just data

it's data describing other data

its' meant for machine consumption

disease name symptoms drug administration

meta-data +

ontologies

<name> <symptoms> <drug> <drug administration> <disease> <treatment> IS-A reduces

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What’s inside an ontology?

terms + specialisation hierarchy classes + class-hierarchy

instances slots/values

inheritance (multiple? defaults?) restrictions on slots (type, cardinality) properties of slots (symm., trans., …)

relations between classes (disjoint, covers) reasoning tasks: classification, subsumption

Increasing semantic “weight”

In short

(for the duration of this tutorial)

Ontologies are

not

definitive descriptions of

what exists in the world (= philosphy)

Ontologies

are

shared models of the world

constructed

to facilitate communication

Yes, ontologies exist

(because we build them)

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Real life examples

handcrafted (often by communities)

z music: CDnow (2410/5), MusicMoz (1073/7) z biomedical: SNOMED (200k), GO (15k),

Emtree(45k+190k)

ranging from lightweight (

Yahoo, UNSPC

)

to heavyweight (

Cyc

)

ranging from small (

METAR

)

to large (

UNSPC

)

allright,

but how to represent all this

in a computer?

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Outline

The vision

What is required

machine representation

z

XML, RDF, OWL

Where are we now?

Examples

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What was XML again?

country

name capital

“Netherlands” name areacode “Amsterdam” “020”

</country>

So why not just use XML?

No agreement on: z structure • is country a: –object? –class? –attribute? –relation? –something else? • what does nesting

mean? z vocabulary

• is country the same as nation?

<countryname=”Netherlands”> <capitalname=”Amsterdam”>

</country> <nation>

<name>Netherlands</name> <capital>Amsterdam</capital> <capital_areacode>

020

</capital_areacode> </nation>

●Are the above XML documents the same? ●Do they convey the same information? ●Is the answer machine-derivable?

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So: XML

≠

machine accessible meaning

CV name education work private < > < > < > < > < > < Χς> <ναμε> <εδυχατιον> <ωορκ> <πριϖατε>

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W3C Stack

XML

:

z Surface syntax, no semantics

XML Schema

:

z Describes structure of XML documents

RDF

:

z Datamodel for “relations” between “things”

RDF Schema

:

z RDF Vocabulary Definition Language

OWL

:

z A more expressive

Vocabulary Definition Language

RDF & RDF Schema

RDF =

z relations between things

z all objects are URL’s (both things and relations)

RDF Schema =

z hierarchical organisation of an RDF vocabulary z all things are URL’s

(classes of things, subclass relations)

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The semantic pyramid again

OWL:

things RDF Schema can’t do

equality

enumeration

number restrictions

z Single-valued/multi-valued z Optional/required values

inverse, symmetric, transitive

boolean algebra

z Union, complement

Again:

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Sounds good in theory.

How far are you with this

in practice?

Where are we now: tools

Languages are stable (W3C)

Tooling is rapidly emerging

z HP, IBM, Oracle, Adobe, … z Parsers,

z Editors, z visualisers,

z large scale storage and querying z Portal generation

Aduna

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Three example use-cases

Closed-world data integration:

DOPE browser @ Elsevier

Open-world data integration:

streaming media @ Philips

Semantic Web services

Conclusions

This section joint with Aduna and

Anita de Waard@Elsevier

This section joint with Aduna and

Anita de Waard@Elsevier

Closed-world data integration:

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Background

Vertical Information Provision

z Buy a topic instead of a Journal ! z Web provides new opportunities

Business driver: drug development

z Rich, information-hungry market z Good thesaurus (EMTREE)

The Data

Document repositories:

z ScienceDirect: approx. 500.000 fulltext articles z MEDLINE: approx. 10.000.000 abstracts

Extracted Metadata

z The Collexis Metadata Server: concept-extraction ("semantic fingerprinting")

Thesauri and Ontologies

z EMTREE:

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RDF Schema EMTREE Query interface RDF Datasource 1 RDF Datasource n

….

Architecture:

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This section material from Zharko Aleksovski @ VU & Philips

Web-based

data integration scenario:

• heterogeneous

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Motivating scenario

consumer.philips.com User devices Semantic Web iTunes Wal*Mart Buy.com Napster eMusic Musicmatch Rhapsody Providers MusicNet MusicNow LaunchCast

Example

Evergreens and Golden hits are related: Golden hits is mostly subclass of Evergreens

Music Ontology

Mediator

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Domain characteristics

Many music providers

Wide variety of music offered

Constantly increasing in size and evolving

Cumbersome to browse and retrieve music

There is no agreement

z Different terms are used

z The same terms contain different sets of artists

CDNow (Amazon.com)

All Music Guide

MusicMoz

ArtistGigs

Artist Direct Network

CD baby

Yahoo

Size: 96classes Depth: 2levels Size: 2410classes

Depth: 5levels _Size:₃₈₂_classes Depth: 4levels Size: 222classes Depth: 2levels Size: 1073classes Depth: 7levels Size: 465classes Depth: 2levels Size: 403classes Depth: 3levels

data-sources

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Why

approximate matching

Genre is not precisely defined

Pop and Rock have no common definition

on the big portals AllMusic.com,

Amazon.com and MP3.com

Exact reasoning will not be useful

A _X % 1 % 99

Results

A - AllMusicGuide B - ArtistDirectNetwork 0 100000 200000 300000 400000 500000 600000 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 B subClass of A A subClass of B equivalences

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This section material from Marta Sabou @ VU

Semantic Web Services

What are web-services

a software system designed to support

interoperable machine-to-machine

interaction over a network.

has an interface described in a machine

processable format (specifically WSDL).

Other systems interact with a web service

in a manner specified by its descriptions

using SOAP messages

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Web Service Tasks

Web Service Discovery & Selection

z Find an airline that can fly me to Marina del Rey

Web Service Invocation

z Book flight tickets from NWAto arrive 12thOct.

Web Service Composition & Interoperation

z Arrange taxis, flights and hotel for travel from

Southampton to Portland, OR, via Marina del Rey, CA.

Web Service Execution Monitoring

z Has the taxi to Gatwick Airport been reserved yet?

Limitations of WS Technology

Manual Discovery

Manual Invocation

Manual (ad hoc) Mediation

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Use of Semantics: Example

<do:HotelBooking rdf:ID=”WS1">

<owls:hasInput rdf:resource=”do:Hotel”/> </do:HotelBooking >

<do:HostelBooking rdf:ID=”WS2"> <owls:hasInput rdf:res=”do:Hostel”/> </do:HostelBooking >

R:(BookingService,Hotel)=> * exact match with WS1 * plug-in match for WS2

Degrees

of WS Matching

Match Advertisement with Request:

Exact: Adv equals Req

Plug-In: Adv is more general than Req

Subsume: Adv is less general than Req Intersection: Adv and Req overlap (a bit)

Disjoint: Adv and Req don’ t overlap

Matchmaking algorithms (primarily) employ subsumption reasoning over the knowledge provided by the domain ontologies.

The Semantic Web: Web of (integrated) Data

The Semantic Web:

Web of (integrated) Data

Take home message



Semantic Web = Web of Data

(no longer only web of text, web of pictures)



Set of open, stable W3C standards



Rapidly emerging tools & vendors



Use cases:

Outline



The vision



What is required



Machine representation

XML, RDF, OWL



Where are we now?



Examples

Things we would

“Intelligent” things

we can’t do today

Other use-case are

Sounds good, so..

how is this tackled?

Outline



The vision



What is required



Machine representation

XML, RDF, OWL



Where are we now?



Examples

machine accessible meaning

(What it’s like to be a machine)

Meta-data !

What is meta-data?



it's just data



it's data describing other data



its' meant for machine consumption

meta-data +

ontologies

What’s inside an ontology?

Increasing semantic “weight”

In short

(for the duration of this tutorial)



Ontologies are

definitive descriptions of

what exists in the world (= philosphy)



Ontologies

shared models of the world

constructed

to facilitate communication



Yes, ontologies exist

(because we build them)

Real life examples



handcrafted (often by communities)



ranging from lightweight (

)

to heavyweight (

)

