SlideShare une entreprise Scribd logo
1  sur  19
Télécharger pour lire hors ligne
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
SEMANTIC SHORTCUTS AND VIEWS:
BRIDGING THE GAP BETWEEN
ONTOLOGIES AND LINKED DATA
Krzysztof Janowicz, Pascal Hitzler, and Adila Krisnadhi
STKO Lab, University of California, Santa Barbara, USA
DaSe Lab, Wright State University, USA
AAG Meeting 2014
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
LINKING DATA AS NEXT-GENERATION INFRASTRUCTURE
Data Silos
Web services
Databases
Web pages
hinder ad-hoc combination
enforce data models
limit re-usability
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
FROM LINKED DOCUMENTS TO LINKED DATA
Use Uniform Resource Identifiers (URI) to identify entities, link them to other
entities, encode information about these entities using the
machine-understandable RDF, and make them available on the Web.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
BERNERS-LEE’S LINKED DATA PRINCIPLES AND STARS
Four Rules for Linked Data
Use URIs as names for things
Use HTTP URIs so that people can look up those names.
When someone looks up a URI, provide useful information, using the standards
(RDF*, SPARQL)
Include links to other URIs. so that they can discover more things.
Is your Linked Open Data 5 Star?
Available on the web (whatever format) but with an open licence, to be Open Data
Available as machine-readable structured data (e.g. excel instead of image
scan of a table)
as (2) plus non-proprietary format (e.g. CSV instead of excel)
All the above plus, Use open standards from W3C (RDF and SPARQL) to
identify things, so that people can point at your stuff
All the above, plus: Link your data to other people’s data to provide
context
See http://www.w3.org/DesignIssues/LinkedData.html
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
EXPLORING AND QUERYING LINKED DATA
EXPLORING LINKED DATA ABOUT PLEACES, PEOPLE, EVENTS
Follow-your-nose: Explore information using Linked Data (DBpedia).
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
EXPLORING AND QUERYING LINKED DATA
HOW TO QUERY LINKED DATA (OVER MULTIPLE SOURCES)?
Integration by searching equivalent classes or/and same features
in data sets. This requires ontologies/vocabularies, their alignment,
and/or ontology reuse.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY STAR RATING
ONTOLOGIES TO MAKE YOUR DATA MORE USABLE
Five Stars of Linked Data Vocabulary Use
Linked Data without any vocabulary.
There is dereferencable human-readable information about the used
vocabulary.
The information is available as machine-readable explicit
axiomatization of the vocabulary.
The vocabulary is linked to other vocabularies
Metadata about the vocabulary is available (in a dereferencable
and machine-readable form).
The vocabulary is linked to by other vocabularies.
See http://semantic-web-journal.net/content/
five-stars-linked-data-vocabulary-use
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY REUSE
VOCABULARY/ONTOLOGY REUSE
A typical statement:
’Reuse external vocabulary whenever possible.’
<http://dbpedia.org/resource/Copernicus_(lunar_crater)>
...
geo:lat
"9.7"^^xsd:decimal;
geo:long
"20.0"^^xsd:decimal;
...
a
dbpedia-owl:Crater,
...
ns5:Place,
...
Concerns:
Most ontologies are under-specific, how are they maintained,
versioning /evolution strategies are unclear, contact persons, are
they community-driven, legal issues, proper documentation,...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY REUSE
REUSE DIFFICULTIES EXAMPLE
The Fluidops interface renders the DBpedia RDF data from the
Copernicus crater and places it on the Surface of the Earth instead of
realizing that the given coordinates are selenographic coordinates.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT
Alternative statement:
’Align your vocabulary to other vocabulary whenever possible.’
dbpedia − owl : Crater ADL: Crater (1)
dbpedia − owl : Person ≡ FOAF : Person (?) (2)
Concerns:
Most ontologies are under-specific, requires reasoning, simple
alignments may not be sufficient (despite improving tool
support),...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
WHAT/HOW TO MODEL?
FRAGMENT OF A MAP LEGEND ONTOLOGY DESIGN PATTERN
Ontological commitments
Should Geographic
Feature Types be classes
or instances?
Do we want to explicitly
define the depictedBy
relation
Is stating that a Legend
consists of LegendItems
redundant?
. . .
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
INSTANCES VS. CLASSES
MODELING DIFFERENCES: INSTANCES VS. CLASSES
As illustrated before alignments and mappings can be difficult
However, often, even major modeling differences can be aligned/mapped
Instances vs. classes
Florence rdf : type City (1)
Florence xyz : hasType ”City”@en (2)
Mapping between those cases
Classname ∃hasType.{classname} (3)
∃hasType.{classname} Classname (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
ROLE CHAINS
FRAGMENT OF THE MAP LEGEND ONTOLOGY
NC = {LegendItem, Symbol, Label, FeatureType} (1)
NR = {consistsOf, isLabelFor, isLabelOf, depictedBy} (2)
¬∃N. (3)
LegendItem ∃consistsOf.Symbol ∃consistsOf.LegendItem (4)
Label ∃SymbolizedBy.Symbol ∀SymbolizedBy.Symbol (5)
≤ 1isLabelFor. (6)
≤ 1isLabelOf. (7)
≤ 1SymbolizedBy. (8)
Label ∃isLabelFor.FeatureType (9)
Label Symbol ⊥ (also for Symbol, Label, FeatureType, LegendItem) (10)
isLabelOf−
◦ isLabelFor depictedBy−
(11)
¬∃consistsOf−
Legend (12)
. . . (13)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
ANOTHER SIMPLE VIEW EXAMPLE
SIMPLE VIEW FOR THE AGENT ONTOLOGY PATTERN STUB
Guarded domain and range restrictions of performsAgentRole
∃performsAgentRole.AgentRole Agent (1)
Agent ∀performsAgentRole.AgentRole (2)
Pairwise-disjointness axiom
Agent AgentRole ⊥ (3)
This axiom provides the role isPerformedBy as a view for the pattern.
performsAgentRole−
isPerformedBy (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

Contenu connexe

Similaire à AAG 2014 Talk on Ontology Views, Reusue, Alignment

Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
Riccardo Albertoni
 
How To Make Linked Data More than Data
How To Make Linked Data More than DataHow To Make Linked Data More than Data
How To Make Linked Data More than Data
Amit Sheth
 
Web Science Synergies: Exploring Web Knowledge through the Semantic Web
Web Science Synergies: Exploring Web Knowledge through the Semantic WebWeb Science Synergies: Exploring Web Knowledge through the Semantic Web
Web Science Synergies: Exploring Web Knowledge through the Semantic Web
Stefan Dietze
 
A Compositional-distributional Semantic Model over Structured Data
A Compositional-distributional Semantic Model over Structured DataA Compositional-distributional Semantic Model over Structured Data
A Compositional-distributional Semantic Model over Structured Data
Andre Freitas
 
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Stephane Fellah
 
Online Relation Alignment for Linked Datasets
Online Relation Alignment for Linked DatasetsOnline Relation Alignment for Linked Datasets
Online Relation Alignment for Linked Datasets
Maria Koutraki
 

Similaire à AAG 2014 Talk on Ontology Views, Reusue, Alignment (20)

Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
Semantic Similarity Assessment to Browse Resources exposed as Linked Data: an...
 
Pl@ntghats IFP
Pl@ntghats IFPPl@ntghats IFP
Pl@ntghats IFP
 
How To Make Linked Data More than Data
How To Make Linked Data More than DataHow To Make Linked Data More than Data
How To Make Linked Data More than Data
 
How To Make Linked Data More than Data
How To Make Linked Data More than DataHow To Make Linked Data More than Data
How To Make Linked Data More than Data
 
Thesaurus alignment for linked data publishing DC 2011
Thesaurus alignment for linked data publishing DC 2011Thesaurus alignment for linked data publishing DC 2011
Thesaurus alignment for linked data publishing DC 2011
 
Biodiversity Informatics on the Semantic Web
Biodiversity Informatics on the Semantic WebBiodiversity Informatics on the Semantic Web
Biodiversity Informatics on the Semantic Web
 
Beyond Linked Data - Exploiting Entity-Centric Knowledge on the Web
Beyond Linked Data - Exploiting Entity-Centric Knowledge on the WebBeyond Linked Data - Exploiting Entity-Centric Knowledge on the Web
Beyond Linked Data - Exploiting Entity-Centric Knowledge on the Web
 
What Are Links in Linked Open Data? A Characterization and Evaluation of Link...
What Are Links in Linked Open Data? A Characterization and Evaluation of Link...What Are Links in Linked Open Data? A Characterization and Evaluation of Link...
What Are Links in Linked Open Data? A Characterization and Evaluation of Link...
 
Web Science Synergies: Exploring Web Knowledge through the Semantic Web
Web Science Synergies: Exploring Web Knowledge through the Semantic WebWeb Science Synergies: Exploring Web Knowledge through the Semantic Web
Web Science Synergies: Exploring Web Knowledge through the Semantic Web
 
Metadata for digital humanities
Metadata for digital humanities Metadata for digital humanities
Metadata for digital humanities
 
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment
 
Archives Hub - Data in :: Data out
Archives Hub - Data in :: Data outArchives Hub - Data in :: Data out
Archives Hub - Data in :: Data out
 
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
 
Geo-Humanities 2017 Keynote at SIGSPATIAL 2017
Geo-Humanities 2017 Keynote at SIGSPATIAL 2017Geo-Humanities 2017 Keynote at SIGSPATIAL 2017
Geo-Humanities 2017 Keynote at SIGSPATIAL 2017
 
A Compositional-distributional Semantic Model over Structured Data
A Compositional-distributional Semantic Model over Structured DataA Compositional-distributional Semantic Model over Structured Data
A Compositional-distributional Semantic Model over Structured Data
 
Vocabularies as Linked Data: SENESCHAL & HeritageData.org
Vocabularies as Linked Data: SENESCHAL & HeritageData.orgVocabularies as Linked Data: SENESCHAL & HeritageData.org
Vocabularies as Linked Data: SENESCHAL & HeritageData.org
 
Semantic Technologies at FAO
Semantic Technologies at FAOSemantic Technologies at FAO
Semantic Technologies at FAO
 
Verifying Integrity Constraints of a RDF-based WordNet
Verifying Integrity Constraints of a RDF-based WordNetVerifying Integrity Constraints of a RDF-based WordNet
Verifying Integrity Constraints of a RDF-based WordNet
 
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
 
Online Relation Alignment for Linked Datasets
Online Relation Alignment for Linked DatasetsOnline Relation Alignment for Linked Datasets
Online Relation Alignment for Linked Datasets
 

Plus de kjanowicz

Big Geo Data
Big Geo DataBig Geo Data
Big Geo Data
kjanowicz
 

Plus de kjanowicz (18)

Debiasing Knowledge Graphs: Why Female Presidents are not like Female Popes
Debiasing Knowledge Graphs: Why Female Presidents are not like Female PopesDebiasing Knowledge Graphs: Why Female Presidents are not like Female Popes
Debiasing Knowledge Graphs: Why Female Presidents are not like Female Popes
 
Golledge Lecture May 2018
Golledge Lecture May 2018Golledge Lecture May 2018
Golledge Lecture May 2018
 
How “Alternative" are Alternative Facts? Towards Measuring Statement Coherenc...
How “Alternative" are Alternative Facts? Towards Measuring Statement Coherenc...How “Alternative" are Alternative Facts? Towards Measuring Statement Coherenc...
How “Alternative" are Alternative Facts? Towards Measuring Statement Coherenc...
 
Building Blocks for Distributed Geo-Knowledge Graphs
Building Blocks for Distributed Geo-Knowledge GraphsBuilding Blocks for Distributed Geo-Knowledge Graphs
Building Blocks for Distributed Geo-Knowledge Graphs
 
Linked (Data) Scientometrics Keynote
Linked (Data) Scientometrics KeynoteLinked (Data) Scientometrics Keynote
Linked (Data) Scientometrics Keynote
 
Ontology Engineering: A View from the Trenches - WOP 2015 Keynote
Ontology Engineering: A View from the Trenches - WOP 2015 KeynoteOntology Engineering: A View from the Trenches - WOP 2015 Keynote
Ontology Engineering: A View from the Trenches - WOP 2015 Keynote
 
Exploring the Data Universe with Semantic Signatures: Plous Lecture 2015
Exploring the Data Universe with Semantic Signatures: Plous Lecture 2015Exploring the Data Universe with Semantic Signatures: Plous Lecture 2015
Exploring the Data Universe with Semantic Signatures: Plous Lecture 2015
 
GeoVoCamp SB 2015 Welcome Slides
GeoVoCamp SB 2015 Welcome SlidesGeoVoCamp SB 2015 Welcome Slides
GeoVoCamp SB 2015 Welcome Slides
 
Ontology Virtualization for Smart Data -- A Semantics Perspective on Open Dat...
Ontology Virtualization for Smart Data -- A Semantics Perspective on Open Dat...Ontology Virtualization for Smart Data -- A Semantics Perspective on Open Dat...
Ontology Virtualization for Smart Data -- A Semantics Perspective on Open Dat...
 
Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...
Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...
Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...
 
'The Why, What, and How of Geo-Information Observatories' GeoRich2014 Keynote
'The Why, What, and How of Geo-Information Observatories' GeoRich2014 Keynote'The Why, What, and How of Geo-Information Observatories' GeoRich2014 Keynote
'The Why, What, and How of Geo-Information Observatories' GeoRich2014 Keynote
 
Heterogeneity is Here to Stay and Semantics is Not About Agreement
Heterogeneity is Here to Stay and Semantics is Not About AgreementHeterogeneity is Here to Stay and Semantics is Not About Agreement
Heterogeneity is Here to Stay and Semantics is Not About Agreement
 
Please don't agree: Introducing Descartes-Core
Please don't agree: Introducing Descartes-CorePlease don't agree: Introducing Descartes-Core
Please don't agree: Introducing Descartes-Core
 
Where is the sweet spot for ontologies?
Where is the sweet spot for ontologies?Where is the sweet spot for ontologies?
Where is the sweet spot for ontologies?
 
GEOSPATIAL SEMANTICS -- PROBLEMS AND PROJECTS
GEOSPATIAL SEMANTICS -- PROBLEMS AND PROJECTSGEOSPATIAL SEMANTICS -- PROBLEMS AND PROJECTS
GEOSPATIAL SEMANTICS -- PROBLEMS AND PROJECTS
 
Semantics and Linked Data for CyberGIS -- AAG 2013 Frontiers and Roadmaps Se...
Semantics and Linked Data for CyberGIS  -- AAG 2013 Frontiers and Roadmaps Se...Semantics and Linked Data for CyberGIS  -- AAG 2013 Frontiers and Roadmaps Se...
Semantics and Linked Data for CyberGIS -- AAG 2013 Frontiers and Roadmaps Se...
 
Big Geo Data
Big Geo DataBig Geo Data
Big Geo Data
 
Introductory slides into Big Data in Geographic Information Science
Introductory slides into Big Data in Geographic Information ScienceIntroductory slides into Big Data in Geographic Information Science
Introductory slides into Big Data in Geographic Information Science
 

Dernier

Porella : features, morphology, anatomy, reproduction etc.
Porella : features, morphology, anatomy, reproduction etc.Porella : features, morphology, anatomy, reproduction etc.
Porella : features, morphology, anatomy, reproduction etc.
Silpa
 
The Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptxThe Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptx
seri bangash
 
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptxTHE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
ANSARKHAN96
 
Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
Silpa
 
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune WaterworldsBiogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Sérgio Sacani
 
biology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGYbiology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGY
1301aanya
 
Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.
Silpa
 

Dernier (20)

TransientOffsetin14CAftertheCarringtonEventRecordedbyPolarTreeRings
TransientOffsetin14CAftertheCarringtonEventRecordedbyPolarTreeRingsTransientOffsetin14CAftertheCarringtonEventRecordedbyPolarTreeRings
TransientOffsetin14CAftertheCarringtonEventRecordedbyPolarTreeRings
 
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
 
Porella : features, morphology, anatomy, reproduction etc.
Porella : features, morphology, anatomy, reproduction etc.Porella : features, morphology, anatomy, reproduction etc.
Porella : features, morphology, anatomy, reproduction etc.
 
The Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptxThe Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptx
 
Site Acceptance Test .
Site Acceptance Test                    .Site Acceptance Test                    .
Site Acceptance Test .
 
Dr. E. Muralinath_ Blood indices_clinical aspects
Dr. E. Muralinath_ Blood indices_clinical  aspectsDr. E. Muralinath_ Blood indices_clinical  aspects
Dr. E. Muralinath_ Blood indices_clinical aspects
 
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptxTHE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx
 
Use of mutants in understanding seedling development.pptx
Use of mutants in understanding seedling development.pptxUse of mutants in understanding seedling development.pptx
Use of mutants in understanding seedling development.pptx
 
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIACURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
 
Cyanide resistant respiration pathway.pptx
Cyanide resistant respiration pathway.pptxCyanide resistant respiration pathway.pptx
Cyanide resistant respiration pathway.pptx
 
PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE
PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICEPATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE
PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE
 
Thyroid Physiology_Dr.E. Muralinath_ Associate Professor
Thyroid Physiology_Dr.E. Muralinath_ Associate ProfessorThyroid Physiology_Dr.E. Muralinath_ Associate Professor
Thyroid Physiology_Dr.E. Muralinath_ Associate Professor
 
Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
 
Grade 7 - Lesson 1 - Microscope and Its Functions
Grade 7 - Lesson 1 - Microscope and Its FunctionsGrade 7 - Lesson 1 - Microscope and Its Functions
Grade 7 - Lesson 1 - Microscope and Its Functions
 
PSYCHOSOCIAL NEEDS. in nursing II sem pptx
PSYCHOSOCIAL NEEDS. in nursing II sem pptxPSYCHOSOCIAL NEEDS. in nursing II sem pptx
PSYCHOSOCIAL NEEDS. in nursing II sem pptx
 
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune WaterworldsBiogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds
 
biology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGYbiology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGY
 
Genome sequencing,shotgun sequencing.pptx
Genome sequencing,shotgun sequencing.pptxGenome sequencing,shotgun sequencing.pptx
Genome sequencing,shotgun sequencing.pptx
 
Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.
 
FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and SpectrometryFAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry
 

AAG 2014 Talk on Ontology Views, Reusue, Alignment

  • 1. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS SEMANTIC SHORTCUTS AND VIEWS: BRIDGING THE GAP BETWEEN ONTOLOGIES AND LINKED DATA Krzysztof Janowicz, Pascal Hitzler, and Adila Krisnadhi STKO Lab, University of California, Santa Barbara, USA DaSe Lab, Wright State University, USA AAG Meeting 2014 SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 2. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS LINKED DATA MOTIVATION LINKING DATA AS NEXT-GENERATION INFRASTRUCTURE Data Silos Web services Databases Web pages hinder ad-hoc combination enforce data models limit re-usability SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 3. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS LINKED DATA MOTIVATION FROM LINKED DOCUMENTS TO LINKED DATA Use Uniform Resource Identifiers (URI) to identify entities, link them to other entities, encode information about these entities using the machine-understandable RDF, and make them available on the Web. SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 4. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS LINKED DATA MOTIVATION BERNERS-LEE’S LINKED DATA PRINCIPLES AND STARS Four Rules for Linked Data Use URIs as names for things Use HTTP URIs so that people can look up those names. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) Include links to other URIs. so that they can discover more things. Is your Linked Open Data 5 Star? Available on the web (whatever format) but with an open licence, to be Open Data Available as machine-readable structured data (e.g. excel instead of image scan of a table) as (2) plus non-proprietary format (e.g. CSV instead of excel) All the above plus, Use open standards from W3C (RDF and SPARQL) to identify things, so that people can point at your stuff All the above, plus: Link your data to other people’s data to provide context See http://www.w3.org/DesignIssues/LinkedData.html SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 5. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS EXPLORING AND QUERYING LINKED DATA EXPLORING LINKED DATA ABOUT PLEACES, PEOPLE, EVENTS Follow-your-nose: Explore information using Linked Data (DBpedia). SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 6. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS EXPLORING AND QUERYING LINKED DATA HOW TO QUERY LINKED DATA (OVER MULTIPLE SOURCES)? Integration by searching equivalent classes or/and same features in data sets. This requires ontologies/vocabularies, their alignment, and/or ontology reuse. SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 7. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY STAR RATING ONTOLOGIES TO MAKE YOUR DATA MORE USABLE Five Stars of Linked Data Vocabulary Use Linked Data without any vocabulary. There is dereferencable human-readable information about the used vocabulary. The information is available as machine-readable explicit axiomatization of the vocabulary. The vocabulary is linked to other vocabularies Metadata about the vocabulary is available (in a dereferencable and machine-readable form). The vocabulary is linked to by other vocabularies. See http://semantic-web-journal.net/content/ five-stars-linked-data-vocabulary-use SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 8. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY REUSE VOCABULARY/ONTOLOGY REUSE A typical statement: ’Reuse external vocabulary whenever possible.’ <http://dbpedia.org/resource/Copernicus_(lunar_crater)> ... geo:lat "9.7"^^xsd:decimal; geo:long "20.0"^^xsd:decimal; ... a dbpedia-owl:Crater, ... ns5:Place, ... Concerns: Most ontologies are under-specific, how are they maintained, versioning /evolution strategies are unclear, contact persons, are they community-driven, legal issues, proper documentation,...? SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 9. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY REUSE REUSE DIFFICULTIES EXAMPLE The Fluidops interface renders the DBpedia RDF data from the Copernicus crater and places it on the Surface of the Earth instead of realizing that the given coordinates are selenographic coordinates. SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 10. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT Alternative statement: ’Align your vocabulary to other vocabulary whenever possible.’ dbpedia − owl : Crater ADL: Crater (1) dbpedia − owl : Person ≡ FOAF : Person (?) (2) Concerns: Most ontologies are under-specific, requires reasoning, simple alignments may not be sufficient (despite improving tool support),...? SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 11. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES a:flowsInto a:IsConnected (1) a:IrrigationCanal a:Canal (2) ∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3) a:Waterbody a:Land ⊥ (4) a:AgriculturalField a:Land (5) b:flowsInto b:IsConnected (6) b:Canal (≥2 b:IsConnected.b:Waterbody) (7) b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody) (=1 b:flowsInto.b:AgriculturalField) (8) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 12. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES a:flowsInto a:IsConnected (1) a:IrrigationCanal a:Canal (2) ∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3) a:Waterbody a:Land ⊥ (4) a:AgriculturalField a:Land (5) b:flowsInto b:IsConnected (6) b:Canal (≥2 b:IsConnected.b:Waterbody) (7) b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody) (=1 b:flowsInto.b:AgriculturalField) (8) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 13. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES a:flowsInto a:IsConnected (1) a:IrrigationCanal a:Canal (2) ∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3) a:Waterbody a:Land ⊥ (4) a:AgriculturalField a:Land (5) b:flowsInto b:IsConnected (6) b:Canal (≥2 b:IsConnected.b:Waterbody) (7) b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody) (=1 b:flowsInto.b:AgriculturalField) (8) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 14. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES a:flowsInto a:IsConnected (1) a:IrrigationCanal a:Canal (2) ∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3) a:Waterbody a:Land ⊥ (4) a:AgriculturalField a:Land (5) b:flowsInto b:IsConnected (6) b:Canal (≥2 b:IsConnected.b:Waterbody) (7) b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody) (=1 b:flowsInto.b:AgriculturalField) (8) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 15. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS VOCABULARY ALIGNMENT VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES a:flowsInto a:IsConnected (1) a:IrrigationCanal a:Canal (2) ∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3) a:Waterbody a:Land ⊥ (4) a:AgriculturalField a:Land (5) b:flowsInto b:IsConnected (6) b:Canal (≥2 b:IsConnected.b:Waterbody) (7) b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody) (=1 b:flowsInto.b:AgriculturalField) (8) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 16. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS WHAT/HOW TO MODEL? FRAGMENT OF A MAP LEGEND ONTOLOGY DESIGN PATTERN Ontological commitments Should Geographic Feature Types be classes or instances? Do we want to explicitly define the depictedBy relation Is stating that a Legend consists of LegendItems redundant? . . . SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 17. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS INSTANCES VS. CLASSES MODELING DIFFERENCES: INSTANCES VS. CLASSES As illustrated before alignments and mappings can be difficult However, often, even major modeling differences can be aligned/mapped Instances vs. classes Florence rdf : type City (1) Florence xyz : hasType ”City”@en (2) Mapping between those cases Classname ∃hasType.{classname} (3) ∃hasType.{classname} Classname (4) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 18. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS ROLE CHAINS FRAGMENT OF THE MAP LEGEND ONTOLOGY NC = {LegendItem, Symbol, Label, FeatureType} (1) NR = {consistsOf, isLabelFor, isLabelOf, depictedBy} (2) ¬∃N. (3) LegendItem ∃consistsOf.Symbol ∃consistsOf.LegendItem (4) Label ∃SymbolizedBy.Symbol ∀SymbolizedBy.Symbol (5) ≤ 1isLabelFor. (6) ≤ 1isLabelOf. (7) ≤ 1SymbolizedBy. (8) Label ∃isLabelFor.FeatureType (9) Label Symbol ⊥ (also for Symbol, Label, FeatureType, LegendItem) (10) isLabelOf− ◦ isLabelFor depictedBy− (11) ¬∃consistsOf− Legend (12) . . . (13) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
  • 19. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS ANOTHER SIMPLE VIEW EXAMPLE SIMPLE VIEW FOR THE AGENT ONTOLOGY PATTERN STUB Guarded domain and range restrictions of performsAgentRole ∃performsAgentRole.AgentRole Agent (1) Agent ∀performsAgentRole.AgentRole (2) Pairwise-disjointness axiom Agent AgentRole ⊥ (3) This axiom provides the role isPerformedBy as a view for the pattern. performsAgentRole− isPerformedBy (4) SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI