slides-semantics for the sea of data

8/7/2019 slides-semantics for the sea of data

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Slide 2IST-2005-027595NeOn-project.org

AgendaAgenda

The Information Integration Problem

Symbol Level vs. Knowledge Level

The NeOn Project

Lifecycle support for network ontologies

The FAO Use Case

Fish Stock Depletion Assessment System

The Solution

Semantic Information Integration with Ontologies


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Problem Statement + Take Home MessageProblem Statement + Take Home Message

Apparently many communities face severe challengeswrt. data integration

Integrating information from multiple sourcescorrectly is difficult for machines

In order to be successful the content must be understood

Semantic technologies / Ontologies provide meansto represent/approximate such an understanding

Most information sources are non-semantic

Lifting them to the knowledge levelmitigates the integration challenge

Integration on conceptual level is much more adequate

Semantic Information Integration is needed


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AgendaAgenda



The NeOn Project


The FAO Use Case


The Solution



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NeOn:Lifecycle support for networkedNeOn:Lifecycle support for networked

ontologiesontologies

Funded by EU: FP6 Integrated Project under Semantics-based knowledge and content systems

14.7 mil project budget over 4 years

The Open University

(co-ordinator)University of Sheffield

Universidad Politecnica

Madrid,

iSOCO,pharmaInnova,

Atos Origin

Universitt Karlsruhe,Software AG,

ontoprise,

Universitt Koblenz

Institut Jozef Stefan

INRIA Alpes

FAO of the UNCNR-LOA


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NeOn: Key Challenges andNeOn: Key Challenges and Project GoalsProject Goals

Key Challenges

Scale of ontologies and datasets

Reuse as a prevailing strategy

Collaboration

Create an open infrastructure for

developing, managing, using

dynamic, networked andcontextualized ontologies

Support and sustain thecommunity

by means of an extensible NeOnToolkit for

engineering and applyingnetworked ontologies

Bootstrap methodology andguidelines

enabling ordinary users to take

advantage of the NeOn tools andNeOn infrastructure


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maschineunderstandable

Machine and humanaccessible

Consense about semanticsof concepts

Definition ofsemantics

Focus on one particularaspects/subset of the world

WhatWhat isis anan OntologyOntology??

AnAnontologyontologyisisaaformalformalandandexplicitexplicitspecificationspecificationof aof a

((sharedshared))conceptualizationconceptualizationof aof adomaindomainofofinterestinterest..

T. GruberT. Gruber


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Ontoprise @ NeOnOntoprise @ NeOn

Ontoprise Competencies:

Inference Engine

OntoBroker

FLogic (object oriented, rule

based)

OWL, RDF(S)

Modelling environment

OntoStudio (base of the NeOnToolkit)

Semantic Media Wiki (HaloExtension)

Create an open infrastructure for

developing, managing, using

dynamic, networked andcontextualized ontologies

Support and sustain thecommunity

by means of an extensible NeOnToolkit for

engineering and applyingnetworked ontologies

Bootstrap methodology andguidelines

enabling ordinary users to take

advantage of the NeOn tools andNeOn infrastructure


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NeOn Web ResourcesNeOn Web Resources

Ontoprise Competencies:

Inference Engine

OntoBroker

FLogic (object oriented, rule

based)

OWL, RDF(S)

Modelling environment

OntoStudio (base of the NeOnToolkit)

Semantic Media Wiki (HaloExtension)

Learn more about the NeOnProject at

www.neon-project.org

Learn more about anddownload the NeOn Toolkit

atwww.neon-toolkit.org
http://www.neon-project.org/http://www.neon-toolkit.org/http://www.neon-toolkit.org/http://www.neon-project.org/


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NetworkNetwork of Ontologiesof Ontologies

O1 O1priorVersionOf

O2

M1,2

relatedWith

sourcetarget

O3 O4

depends

On

O1

incompatibleWith

M1,2

source

exte

nds

priorVersionOf

A Network of Ontologies is a collection of ontologies related together viaa variety of different relationships such as mapping, modularization,version, and dependency relationships.

We call the elements of this collection Networked Ontologies.


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The Use CaseThe Use Case

Food and Agriculture Organization of the United Nations

http://www.fao.org/, esp. its Knowledge and Communication Department

Fisheries Department

Fish Stock Depletion Assessment System (FSDAS) Decision support system for fisheries managers

Help discover and assess resources related to stock depletion

Integrate and align between classification systems:

FIGISFact sheet schemas

ISO, UNLand areasISSCFC, EU Harmonized, ISTCCommodities

FAO statistical areaAreas

AgroVoc, ASFAThesauri

ISSCFGGear types

ISSCFVVessel types

ISSCAAP, FAO taxonomicSpecies
http://www.fao.org/http://www.fao.org/


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Classification Systems areClassification Systems areOntologizedOntologized

species

water areas

territories

gears

AgroVoc

vessels

commodities


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Domain ontologies crossDomain ontologies cross--mapped to createmapped to createcompound ontologiescompound ontologies

fish lives in water area water area isgoverned by

territory

fish is fishedwith gear

gear is

on vessel

fish has synonyms and

names in other languages

species

water areas

territories

gears

AgroVoc

vessels

commodities

commodityoriginates from fish


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KB is Populated by IntegratingKB is Populated by Integrating

Existing Information SystemsExisting Information Systems

species water areascommodities

fish stocks

user

geo-spatial data

document repositoriestime-series statistics

economic reports

synonym expansion

AgroVoc


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Building

from existing thesauri, classificationschemes, glossaries, etc

Editing

multiple ontology editing

workflow

annotations

Ontologies to model classificationsschemas and thesauri

Population from existing RDBMS

Biological species: 44,100

Water bodies: 1,500

Land areas: 25,000

ASFA thesaurus: 22,000 AGROVOC thesaurus: 300,000

Commodities: 6,000

FSDAS = Fish Stock Depletion Assessment SystemFSDAS = Fish Stock Depletion Assessment System


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AgendaAgenda



The NeOn Project


The FAO Use Case


The Solution



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FSDAS Server

sources 1n

OverviewOverview

ClientApplicationquery

responses


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sources 1n

generatedontologies

manual mappings

integrationontology

Approach (Design Time)Approach (Design Time)

automaticschemamapping

Import from

DBSchema from RDBMS XMLSchemas

WSDL files


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Tool Support (Design Time)Tool Support (Design Time)

Ontology Engineering environment

NeOn Toolkit / OntoStudio

Supports different ontology languages

FLogic

OWL

RDF(S)

Provides rich feature set and is extendible by plug-ins (Eclipse based)

Relevant for Semantic Information Integration

Ontology modeling

Automatic ontology generation based on non-ontological sources

Mapping perspective to manually link multiple ontologies


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Modeling Support (Design Time)Modeling Support (Design Time)


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Mapping Support (Design Time)Mapping Support (Design Time)


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Problem Statement + Take Home MessageProblem Statement + Take Home Message

Apparently many communities face severe challenges

wrt. data integration

Integrating information from multiple sourcescorrectly is difficult for machines

In order to be successful, the content must be understood

Semantic technologies / Ontologies provide means

to represent/approximate such an understanding

Most information sources are non-semantic

Lifting them to the knowledge levelmitigates the integration challenge

Integration on conceptual level is much more adequate

Semantic Information Integration is needed


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Visit theVisit the

NeOn websiteNeOn website

www.neonwww.neon--project.orgproject.org

Thank you!!!

[email protected]

Download theDownload the

NeOn ToolkitNeOn Toolkit

www.neonwww.neon--toolkit.orgtoolkit.org
mailto:[email protected]:[email protected]

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slides-semantics for the sea of data