Semantic Web Vision, Technologies, Applications...6 Tekoäly, Eero Hyvönen, 2004 11 Roget’s Thesaurus: example • Top level: 6 classes • 1. Abstract relations • 2. Space •

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Semantic WebVision, Technologies,

ApplicationsProf. Eero Hyvönen

Helsingin yliopisto ja Tietotekniikan tutkimuslaitos HIITSemantic Computing Research Group

http://cs.helsinki.fi/group/seco/

Tekoäly, Eero Hyvönen, 2004

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Outline

• WWW as publication channel– Where are we now?

• Semantic web– Ontologies constitute the core

• Applications & demos

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Ontologies – Perspectives

– Philosophy– Linguistics– Computer Science


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Perspectives of ”ontology”:Philosophy

• Study of the essence of Being – apart from the particular existing things

• Plato’s world of ideas: metaphysics• Artistotle’s (384-322 B.C.) 10 Categories• Medieval logicians: first semantic net

– Genus (supertype) vs. species (subtype)• “Ontology” as a discipline

• R. Göckel, J. Lorhard, 1613• Kant (1787), Peirce, Husserl, Whitehead,

Heidegger, …

• Today: theoretical bias– foundational categories & logic behind

everything

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Aristotles’ 10 categories

The cat satPositionThe cat has a ratHavingThe cat is runningActionThe cat desires fishPassion

The cat came out yesterdayWhenThe cat is in the houseWhereThe cat is half the size of …RelationThe cat is 50cm highQantityThe cat is blackQualityA catSubstance


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Hierarchical categories: Tree of Porphyry of Aristotle’s ”Substance”

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Aristotle’s Syllogisms• Four types of

propositions:

• Examples of syllogisms:

(Sowa, 2004)


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Formal ontology• Branch of philosophy

– Using formal methods in the study of being– Developing formal (logical) ontological theories– Combination of philosophy and AI

• Theories– Theory of parts & wholes– Theory of time– Naiive physics– …

• Domain dependent vs. independent ontologies• Descriptive vs. formal ontologies

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Perspectives of ”ontology”:Linguistics

• Meanings vs. words• Peter Mark Roget’s Thesaurus 1852

– Tool for analysis and classification of ideas that helps human communication

• Terminology: dictionaries & vocabularies– Concept analysis

• Thesauri– Indexing/classifying/retrieving data– Language translation

• Semantic thesauri– WordNet, EDR (Electronic Dictionary

Research),…


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Roget’s Thesaurus

• Everything in 1000 categories– Nouns, adjectives, verbs, …

• 1975: over 100,000 words– Neighboring categories semantically related

• e.g. 266=”Journey”; 267=”Navigation”– Not a formal model

• Only for human interpretation

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Roget’s Thesaurus: example• Top level: 6 classes

• 1. Abstract relations• 2. Space• 3. Matter• 4. Intellect• 5. Volition• 6. Affections

• CLASS 2. SpaceI Space in general

Abstract space180 Indefinite space {Noun: space, extension, extent, expanse,…

Verb: reach, extend,…Adj: spacious, roomy, …Adv: extensively, …}

181 Definite region …182 Limited space …

II Relative space183 Situation ……


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Terminology

• Based on concept analysis of word meanings• Standardized methodology

– Various ISO standards• Well-defined vocabularies for human users, not

for machines– In Finland: e.g. Sanastokeskus TSK

• Following presentation is based on:– Heidi Suonuuti: “Guide to terminology”, 2001

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Concept analysis:Extended Odgen-Richards triangel to tedraed

Object

Definition

Terms

Concept

treeBaumarbrepuu

”tall plant with hard self-supporting trunck and …”


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Concept vs. term• Monosemy

– one term - one concept• Polysemy

– one term - many related concepts• E.g. “head” (of arrow) vs. “head” (of human)

• Homonymy– one term - many unrelated concepts

• E.g. “kuusi” (number vs. tree)

• Synonymy– One concept – many terms

• E.g. “honka”=“mänty”

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• Characteristics (properties)– Delimiting characteristics differentiate concepts

• E.g. concept “tree”: – “have a root” not delimiting– “have a self-supporting trunk” delimiting

• Intensional and extensional definitions– Intension = sum of general characteristics

• Tree= “long-living plant, have a self-supporting trunk, …”

– Extensional = list of objects• Tree= {pine, maple, spruce, …}


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Concept systems

• Concepts are not independent• Relations between concepts

– Generic (hyponyny)– Partitive (meronymy) – Associative (function, et.)

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Generic relation (hyponymy)

• Concepts share general characteristics but one• Concept hierarchy: Super/subordinate

• Problem: several branching possibilities– Anatomy: coniferous vs. broad leaf– Requirements: light-demanding vs. tolerant– Abscission: evergreen vs. deciduous

tree

coniferous tree broadleaf tree

pine spruce … birch maple …


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Representing parallel independent subdivisions

tree

evergreen deciduoustree tree

abscissionanatomy

requirements

coniferous broadleaftree tree …

pine spruce …

Using three subdivision dimensions:

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Partitive relation (meronymy)• Part-whole relation

– Examples:• Atoms in a molecule• Legs of a chair

– Optional, single, and multiple parts

– Also along different dimensions• Tree -> permanent vs. non-permanent organs

tree

root trunk branch


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Different meronymy relations

childhood/growing-upphase /processHelsinki/Finlandplace / region

aluminium/airplanematerial / objectpiece-of-cake/cakepiece / wholetree/forestmember / setbranch/treepart / whole

(C. Fellbaum, 1998)

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Associative relations

nest / treeobject / location

etc…etc…wood / papermaterial / productpaper machine / paper makingtool / functionapple tree / fruit gatheringobject / activity

nesting / treeactivity / locationnesting / birdactivity / actormagpie / nestproducer / productspring / leafs in treescause / effect


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Associative relations

• Arrow notation

pulping

tree wood

process / material

origin / material

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Term definition

• Based on:– Concepts in the concept system– Relations among the concepts

• Intensional definitions– Idea: describe only delimiting characteristics– Other characteristics come from the hierarchy

• “noble gas”: gas that in the natural state is inactive

• Extensional definitions: list objects• “noble gas”: helium, neon, argon, krypton, and radon


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• Principle of substitution– Terms and definitions are interchangeable

• Deficient definitions– Circular definitions

• Evergreen tree: – Bad: tree with evergreen foliage– Good: tree that retains its foliage throughout the year

– Negative definitions• Deciduous tree:

– Bad: other than evergreen tree– Good: tree losing its foliage annually

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– Incomplete definitions• Too broad:

– Tree: tall plant that lives for many years» Covers banana, wine, …

• Too narrow:– Fertility: ability of a tree to produce offspring

» Also magpies can be fertile

– Hidden definitions• Several definitions in a concept definition


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Terms

• Term types– Word

• Nouns, adjectives, verbs• “house”, “biodegradable”, “reclaim”

– Compound• “lighthouse”

– Multiword expressions• “broadband integrated services digital network”• ”kolmivaihekilowattituntimittari”

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• Preferred terms– Usually only one preferred term per concept– Other synonyms: admitted / deprecated

• Selection of terms– Concepts in a subject field– Concepts used in several subject fields– Concepts borrowed from adjacent fields– Concepts used in general language

• Harmonization– Identical terms between different fields


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Why terminology

• Provides useful tools and ideas for concept analysis and definition

• Normative goal– Analyze, select, harmonize, and define a concise set

of terms to be used in human communications• Does not provide formal enough descriptive

representations for machine semantics– Methodology is useful there, too

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”Classical” thesauri• Semantically arranged

terminologies/dictionaries– Terms may be a mixture of words and concepts

• Based traditionally on the following relations– BT Broader term

NT Narrower termRT Related termUSE “See”UF Used for; reciproc. USESN Scope note

• E.g. in Finland:– Yleinen Suomalainen Asiasanasto, MASA, MUSA,

Allers, …


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• Used especially for– Indexing information content (keywords)– Information retrieval

• Keyword search• Term expansion: “tree” -> “pine”, “birch”,…

• Widely used, lots on indexed data– E.g. libraries, museums, archieves, …

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Limitations

• Meaning of relations– BT/NT for sub/superordinate, part-of etc.– RT has lots of different interpretations

• Cause/effect, tool/product, …• Cf. associative relation in terminology

• Not formal enough for computers– E.g. delimiting characteristics implicit– Semantics vague


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Esimerkki:Tesaurus vs. ontologia

– SisustusesineetST Peilit

PeilitST Taskupeilit

– Ok, mutta kyselyn ”Hae sisustusesineet”tulokseen tulevat myös taskupeilit!

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Ontology

Computer Science Perspective


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• Information Systems– 1967 G. H. Mealy

• Relating data with the real world– Object-oriented programming

• The main paradigm in practice since the late 90’s

• Artificial Intelligence– Since 60’s– Natural Language Understanding – Knowledge Representation

• Logic + Ontologies + Computations

• WWW and the Semantic Web– Since late 90’s

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Ontologian käsite

• “Ontologia on formaali, eksplisiittinenmäärittely yhteisestä käsitteistöstä”(Gruber, 1993)

• Formaali: jämpti• Eksplisiittinen: jopa konekin voi ymmärtää• Yhteinen: kommunikaatio mahdollista

• Ontologia kuvaa– sovellusmaailmassa olevat käsitteet/oliot ja– niistä käytettävän sanaston


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• Komponentit– Käsitteiden määrittely: koneen ymmärtämällä

tavalla– Terminologia: ihmisten ymmärtämällä tavalla

• Edellytys sille, että ihmiset ja koneet voivatymmärtää toisiaan

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Glossary- word list- little structure

Thesaurus- relations- NT, BT, RT etc.

Taxonomy- relations- inheritance- constrains

Axiomatized theory- formal system- logic-based

Ontological complexity/depth

Hum

anun

ders

tand

able

Mac

hine

un

ders

tand

able ONTOLOGY TYPES

Numbers

Philosophical text


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MAO

abstraktit käsitteettoimijat

tapahtumatmateriaalit ja aineet

esineetarkisto- ja kirjastoaineisto

organismitympäristöt

AAT AAT ArtArt & & ArchitectureArchitecture ThesaurusThesaurus-- Paul Getty Paul Getty --ssäääätitiöön n thesaurusthesaurus-- 7 p7 pääääluokkaa, 125 000 kluokkaa, 125 000 kääsitettsitettää

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Museum ontology MAO


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IEEE Standard Upper MergedOntology (SUMO)

• Goals– Automated reasoning support in knowledge-based applications– Interoperability

• Define new data elements using SUMO and obtain mutualinteroperability

• Interoperability between applications using domain specificontologies (that use SUMO)

• Neutral interchange format for different systems

• Application areas– E-commerce– E-learning– Natural langugage understanding tasks– …

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SUMO Principal Distinctions


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SUMO Object:

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Standard Upper Merged Ontology


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SUMO Knowledge Representation• Developed in KIF (Knowledge Interchange

Format)– A version of first order predicate logic– Other versions exist (e.g. OWL)

• Size– 1006 terms– 4142 axioms– 814 rules

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Cyc Ontology• Idea: enCYClopeadic knowledge base

– Common sense knowledge• Goal: emergent intelligence by quantity (rather

than by quality)– Over 100.000 concepts– About 1.000.000 facts & axioms in logic

• www.cyc.com– Top parts of the ontology are available for free

• Garbage in -- garbage out?


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Cyc ontology

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Some Difficulties in Ontology Engineering

• How to select individuals, subclasses & properties?– GeorgeBush < … < Process

• How to define them?– Collection: Intangible, Imperciple?

• Scaling problems: 1,000,000 facts• Different needs in different applications

– How to reuse and share knowledge?


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• How to align or merge ontologies?• How to deal with changing ontologies?

– Versioning– Ontology time series

• How to deal with imprecise ontologies?

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Ontology reuse & sharing

– Special ontologies for specific microworld applications– E.g. River < Line

• but for a ship also river depth matters!– How share ontologies in different applications?

• More generic KB framework is needed

(Warren, Pereira, 1982)


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Ontology alignment

Know

Wissen = Savoir(knowing-that)

Kennen = Connaitre

River(size = big)

Fleuve(river running into sea)

Riviere(river or stream running into anotherriver)

Stream(size = small)

BigRiviere

(Sowa, 2001)

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Ontology alignment

(Sowa, 2004)


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Adjective-noun concepts– Prehending entity

• Property modifies directly the object (class instance)• “old man” = the person is old

– Prehended entity• Property modifies the class• “nuclear physicist” = the person isn’t “nuclear”

– Intention• modifies the relation• “former president” = the person is not former, but was the

president

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Example– “Sibelius is a good musician …

• good(Sibelius) & musician(Sibelius)– … and a bad cook”

• bad(Sibelius) & cook(Sibelius)– => Is Sibelius is a bad musician? -> Yes!

– The problem:• good/bad modify class musician/cook• musician/cook modify object Sibelius


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Ontology in computer science: what is needed?

• Formal expressive language for defining concepts and terms, their mutual relations, and inferences– Procedural systems: object-oriented programming

• Java, C++, …– Declarative systems (logic based)

• Logic: RDF(S), KIF, Prolog, OWL,…

• Ontological theory represented in it• E.g. Cyc

• (Application making use of (interpreting) the theory)

• Procedural semantics

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3-Dec-04 1

Towards Intelligent Web Services

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Bringing the web to its full potential

Semantic W

eb enabled W

eb Services

Static

Dynamic UDDI, WSDL, SOAPWeb Services

URI, HTML, HTTP RDF, RDF(S), OWLWWW Semantic Web

Intelligent WebServices

The General Vision

(Dieter Fensel, 2002)

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Web Generations

1G WWW:WWW-pages for human interpretationHTML

2G WWW: Structures for human/machine interpretationXML

3G WWW: Semantic WebMeanings for human/machine interpretationRDF(S), OWL

=> New basis for intelligent WWW-services

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Semantic Web & Web Services:

Teknologioita

3-Dec-04 4

Semantic Web:Technology push

Internet levelUnicode, URI,...

Structure levelXML, XML DTD/ Schema, XSL,...

Metadata levelRDF, Topic Maps,...

Ontology levelRDFS, OWL, WordNet, ...

Logic levelKIF, RuleML, SWRL, ...

Trust levelDigital signature, annotations,... Planning

CPR, SPAR, PDDL,…

ProcessesBPML, WPDL, PSL,…

ServicesUDDI, WSDL, OWL-S,…

TransactionsXML/EDI, KQML,…

CommunicationTCP/IP, HTTP, SOAP,...

Laajennettu Tim Berners-Leen (W3C) ”teknologiakakku”

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Metadata level

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RDF Resource Description FrameworkRDF draft 1999, recommendation 2.10.2004– W3C recommendation 10.2.2004– For representing metadata– Relational data model, not a syntax like XML

RDF-description = directed graphCan be serialized by XML

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RDF(S)

RDF Resource Description Framework (1999)– General framework for describing web resources – Specification: Model & syntax– Relational model, not a syntax like XML

RDF Schema (2000)– For defining RDF- vocabularies– Object-oriented descriptions for WWW languages

Class hierarchies, properties, constraints, inheritance (Class/subClass/type)

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RDF Model

Idea: metadata about WWW resources (URIs)Object-Attribute-Value triples– "Helsinki"-"is located in"-"Finland"– <http://www.helsinki.fi, Location, http://www.finland.fi>

Reification– “Hierarchical” statements about statements

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RDF SyntaxXML-based (other serializations exist, too!)" The WWW-paper "Using RDF …" was written by John Smith, concerns RDF, Metadata and Semantic Web", and was created on Jan 1, 2000"

<?xml version="1.0"?><rdf:RDF xmlns:rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:DC = "http://purl.org/dc/elements/1.1/"> <rdf:Description rdf:about = "http://www.helsinki.fi/john.smith/paper.html"> <DC:Title>Using RDF to describe web resources</DC:Title> <DC:Creator>John Smith</DC:Creator> <DC:Date>2001-01-01</DC:Date> <DC:Subject>RDF, Metadata, Semantic Web</DC:Subject> </rdf:Description></rdf:RDF>

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Corresponding RDF model (graph)

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Creating RDF descriptions

RDF is a language for the machine, not for end-user (like HTML)RDF-generation:– From databases (hidden web)– From document metadata

E.g. PDF– From higher level languages

E.g. DAML+OIL, OWL ontology languages– By metadata editors

E.g. RDFPic

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Why RDF is needed?

Generalizing the underlying model– XML model is a tree– RDF model is a graph

The arcs in graph do not have ordering (like in a tree)External metadata descriptions possibleGraphs can be merged

– Looks like a tiny detail but is an important stepGeneral "framework“, not domain specific

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RDF Example

(Maedche, 2002)

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RDF Schema

– W3C draft 2000, recommendation 10.2.2004– Defines RDF vocabularies– Object oriented modeling for WWW

Classes, instances, propertiesHierarchies, inheritance (Class/subClass/type)Property contraints (domain, range)

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RDF(S) Example

(Maedche, 2002)

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Ontology level

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OWL Web Ontology LanguageW3C Standard– Next level above RDF(S)

Based on formal (description) logic– Inference, consistency– Subsumption: find objects satisfying a description– Subset of predicate logic

Optimized for subsumbtion relation & decidability

Human-friendly tools being developed– RDF(S) is produced by the machine

Based on– USA: DAML– EU: OIL

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class-def animalclass-def plant

subclass-of NOT animalclass-def tree

subclass-of plantclass-def branch

slot-constraint is-part-of has-value treeclass-def leaf

slot-constraint is-part-of has-value branchclass-def defined carnivore

subclass-of animalslot-constraint eats value-type animal

class-def defined herbivoresubclass-of animalslot-constraint eats

value-type plant OR (slot-constraint is-part-of has-value plant)class-def herbivore

subclass-of NOT carnivoreclass-def giraffe

subclass-of animalslot-constraint eats

value-type leafclass-def lion

subclass-of animalslot-constraint eats value-type herbivore

class-def tasty-plantsubclass-of plantslot-constraint eaten-by has-value herbivore, carnivore

EXAMPLE OF AN OIL ONTOLOGY

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Why OWL?Terminology logics for defining ontologiesUsage– Design phase

Check consistencyDerive subsumption hierarchy

– Data integrationDetect inconsistenties and unintendent relations

– DeploymentTerm expansion and inference, e.g. in information retrievalUsing descriptions in applications

Generic tools for cross-domain applications– E.g. Protégé OWL Plugin

Open standardW3C Recommendation

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Three different versions of OWL– OWL Lite

Classifications + simple constraints– Local contraints on property values- cardinality 0 or 1

- OWL DL (description logic)- Complex classes

- unionOf, intersectionOf, compmentOf- Enumerated and disjoint classes

- oneOf, disjointWith- Classes defined on particular property values

- hasValue- OWL Full

- Classes as instances- Free cardinality

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OWL example

<owl:Class rdf:ID="Wine"><rdfs:subClassOf rdf:resource=

"&food;PotableLiquid" />...<rdfs:subClassOf><owl:Restriction><owl:onProperty

rdf:resource="#hasMaker" /><owl:allValuesFrom

rdf:resource="#Winery" /></owl:Restriction>

</rdfs:subClassOf>...

</owl:Class>

Local property value contraints (not possible in RDFS)- Maker of Wine must be a Winery; maker of chairs smthg elseImporting food-ontology

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OWL vs. RDFSClass definitions

Lattice hierarchy of concepts between classes Thing, NothingLogical combinations of anonymous classes

– unionOf, intersectionOf, complementOfEquality

Property restrictionsQuantification & class based -constraints

– allValuesFrom, someValuesFromCardinalityValue restrictions: hasValue

Property typesTransitive, Symmetric, Functional, Inverse, Equal

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A Good OWL-tutorial and Tool

3-Dec-04 24

WHAT IS NEW?

Object-orientedmodeling

Knowledge representationLogic based semantics

XML-syntax, e.g., RDF(S)

PROGRAMMING ARTIFICIAL INTELLIGENCE

WWW-TECHNOLOGIES

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Applications

Yhteentoimivuus (interoperability)Informaation haku (information retrieval)Tietämyksen hallinta (knowledge management)Web palvelut (Web Services) sovelluksineenProfilointi ja kustomointi (profiling, customizing)

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Open Directory ProjectLinux-henkinen hanke tuottaa metadataa web-sisällöistä– yli 65.000 toimittajaa eri aloilla

Metatiedon yhdistely perustuu yhteiseen RDFS ontologiaan– 590.000 kategorian hierarkia!

Isot mittasuhteet – yli 4 miljoonaa annotoitua sivua

Yahoo-tyyppinen hakukone

(Tilanne 11.11.2004)

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Open Directory Projecthttp://dmoz.org

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Google ja ODP

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TAP Ontology and News

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TAP News and Related Stuff

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Magpiehttp://kmi.open.ac.uk/projects/magpie/main.html

Ontologiat

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Case: Promoottori– Photo repository of the Promotion Ceremonies

at the University of Helsinki– Exhibition to be opened at the Senate Square

at Helsinki city center 10/2003The Helsinki University Museum

– An interesting view into thelife of the university

– Complicated semantics of obscure events

– Novice users who do not know the contents before

– Team: Eero Hyvönen, Samppa Saarela, Kim Viljanen, Arvil Styrman, Jaana Tegelberg, …

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12/3/2004

Case Case MuseoSuomiMuseoSuomihttphttp://://museosuomi.cs.helsinki.fimuseosuomi.cs.helsinki.fi

12/3/2004 © Eero Hyvönen

The Vision of The Vision of MuseumFinlandMuseumFinland

1.1. Global View to Distributed CollectionsGlobal View to Distributed Collections•• OneOne seamlessseamless national national collectioncollection ((virtuallyvirtually))•• ””MuseumsMuseums in Finland” in Finland” --> ”> ”MuseumMuseum of Finland”of Finland”

2.2. IntelligentIntelligent ServicesServices to to EndEnd--UsersUsers•• Search: ConceptSearch: Concept--Based Information RetrievalBased Information Retrieval•• Browsing: Semantically Linked ContentsBrowsing: Semantically Linked Contents

3.3. Easy Content Publication for MuseumsEasy Content Publication for Museums

CreatingCreating a national a national platformplatform and a and a processprocess for the for the museumsmuseums to to publishpublish theirtheir contentcontent togethertogether on on

the the SemanticSemantic WebWeb

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1. Global View to Distributed 1. Global View to Distributed CollectionsCollections

Single accessSingle access--point to all collectionspoint to all collectionsSingle user interface to be learnedSingle user interface to be learnedTypicalTypical approachapproach todaytoday: : MultiMulti--searchsearch ((MetaMeta--searchsearch))–– E.gE.g. Australian . Australian MuseumsMuseums onlineonline


Australian Museums onlineAustralian Museums online

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SearchSearch ResultResult


ProblemsProblems

HowHow to to findfind the the rightright keywordskeywords??LikelyLikely resultsresults: : nono--hitshits oror 1000 1000 hitshitsTooToo manymany irrelevantirrelevant hitshits ((lowlow precisionprecision))TooToo fewfew relevantrelevant hitshits ((lowlow recallrecall))HowHow to to getget overviewsoverviews of the of the contentscontents??HowHow to to findfind relatedrelated objectsobjects??Is Is thisthis entertainingentertaining & & educatingeducating??

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MuseumFinlandMuseumFinland ApproachApproach


2. 2. IntelligentIntelligent ServicesServices to to EndEnd--UsersUsers

MuseumFinlandMuseumFinlandDemonstrationDemonstration

1.1. MultiMulti--facetfacet searchsearchBasedBased on on ontologiesontologies

2.2. SemanticSemantic browsingbrowsingBasedBased on on logiclogic

3.3. KeywordKeyword searchsearchBasedBased on on conceptsconcepts http://museosuomi.cs.helsinki.fi

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MuseumFinlandMuseumFinlandMobile VersionMobile Version

WAP 2.0 WAP 2.0 compatiblecompatible phonesphonesNokia Nokia SeriesSeries 60 60 browserbrowserCombiningCombining coordinatescoordinatesand and locationlocation ontologyontologyGettingGetting moremore info info bybyemailemailSameSame URL:URL:http://http://museosuomi.cs.helsinki.fimuseosuomi.cs.helsinki.fi


3. Easy Local Content 3. Easy Local Content PublicationPublication

–– The simple publishing idea of the WWWThe simple publishing idea of the WWW–– The museum creates RDF and publishes it in a The museum creates RDF and publishes it in a

public directory (or on CD)public directory (or on CD)ContentContent creationcreation toolstools: : TerminatorTerminator, , AnnomobileAnnomobileContentContent creationcreation hashas itsits ownown challengeschallenges

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Summary:Summary:Why Why MuseumFinlandMuseumFinland??MuseumMuseum Visitor’sVisitor’s viewpointviewpoint

SeamlessSeamless viewview to to heterogeneousheterogeneous collectionscollectionsIntelligentIntelligent ServicesServices

SearchSearch based on views and based on views and ontologiesontologiesBrowsingBrowsing based on semantic recommendationsbased on semantic recommendations

Museum’sMuseum’s viewpointviewpointPublicationPublication channelchannel for the for the SemanticSemantic WebWebOnlyOnly contentcontent needsneeds to to bebe createdcreated

WillWill bebe enhancedenhanced withwith new new ontologiesontologies and and materialsmaterials

MuseumFinlandMuseumFinland = = DemonstrationDemonstration on on howhow to to createcreate a national a national culture culture contentcontent publicationpublication channelchannel for the for the SemanticSemantic WebWeb


AwardsAwards to to MuseumFinlandMuseumFinland 20042004

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FromFrom MuseumFinlandMuseumFinland to to CultureFinlandCultureFinland and and beyoundbeyound

New New kindskinds of of semanticsemantic materialmaterial–– ContentContent annotationannotation of of artart and and photosphotos

CurrentlyCurrently paintingspaintings onlyonly as as artifactsartifactsThe National The National GalleryGallery and ICONCLASSand ICONCLASSThe The FinnishFinnish MuseumMuseum of of PhotographyPhotography

–– ContentContent annotationannotation of (of (educationaleducational) ) videosvideosThe The FinnishFinnish BroadcasringBroadcasring Company (YLE)Company (YLE)

–– ContentContent annotationannotation of of narrativesnarratives and and processesprocessesThe national The national eposepos ”Kalevala””Kalevala”

Motto: ”Motto: ”MuseumFinlandMuseumFinland --> > CultureSampoCultureSampo””


ThanksThanks

Documents

Semantic Web Vision, Technologies, Applications...6 Tekoäly, Eero Hyvönen, 2004 11 Roget’s Thesaurus: example • Top level: 6 classes • 1. Abstract relations • 2. Space •