slides-semantics for the sea of data
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Transcript of slides-semantics for the sea of data
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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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Slide 3IST-2005-027595NeOn-project.org
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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Slide 4IST-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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Slide 5IST-2005-027595NeOn-project.org
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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Slide 6IST-2005-027595NeOn-project.org
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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Slide 7IST-2005-027595NeOn-project.org
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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Slide 8IST-2005-027595NeOn-project.org
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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Slide 9IST-2005-027595NeOn-project.org
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/ -
8/7/2019 slides-semantics for the sea of data
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Slide 10IST-2005-027595NeOn-project.org
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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Slide 12IST-2005-027595NeOn-project.org
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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Slide 13IST-2005-027595NeOn-project.org
Classification Systems areClassification Systems areOntologizedOntologized
species
water areas
territories
gears
AgroVoc
vessels
commodities
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Slide 14IST-2005-027595NeOn-project.org
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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Slide 15IST-2005-027595NeOn-project.org
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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Slide 16IST-2005-027595NeOn-project.org
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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Slide 17IST-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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Slide 18IST-2005-027595NeOn-project.org
FSDAS Server
sources 1n
OverviewOverview
ClientApplicationquery
responses
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Slide 19IST-2005-027595NeOn-project.org
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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Slide 20IST-2005-027595NeOn-project.org
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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Slide 21IST-2005-027595NeOn-project.org
Modeling Support (Design Time)Modeling Support (Design Time)
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Slide 22IST-2005-027595NeOn-project.org
Mapping Support (Design Time)Mapping Support (Design Time)
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Slide 24IST-2005-027595NeOn-project.org
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!!!
Download theDownload the
NeOn ToolkitNeOn Toolkit
www.neonwww.neon--toolkit.orgtoolkit.org
mailto:[email protected]:[email protected]