Modular Ontology Architecture for Data Integration inthe GeoLink Project
Adila Krisnadhi
Wright State University
Ontology Summit 2016
Krisnadhi GeoLink Data Integration Ontology Summit 2016 1 17
Motivation
Needed
Data integration providing unified view over data at different sources
Krisnadhi GeoLink Data Integration Ontology Summit 2016 2 17
DI Challenges and GeoLink Project
Challenges (regardless of architecture)
Syntactic heterogeneity different data formats serializationsSemantic heterogeneity different vocabulary different level ofgranularity in data different conceptualizationSocialnon-technical inabilityunwillingness to participate fear ofunanticipated cost worry with major changes in their local systemskeptic with scalability
GeoLink Project (wwwgeolinkorg)
Part of NSFrsquos EarthCube Program ndash one among dozens of buildingblock projectsLinked Data + Ontology design pattern-based integration
Krisnadhi GeoLink Data Integration Ontology Summit 2016 3 17
Why Ontology Patterns
Upper-level and many domain ontologies are
Hard to understand mdash too many terms too abstract too complicatedaxioms too far from real dataImpose ontological commitments that may not be acceptable by allpartiesBrittle mdash costlyhard to extend carelessly extending may cause thewhole thing breaks
Ontology design pattern (ODP) a (ldquoreusablerdquo) solution of afrequently occurring modeling problem in the domain and can act asa building block of a more complex ontology
Content pattern (CP) an ODP that models a particular genericnotion in a particular domain
Community engagement via collaborative modeling
Krisnadhi GeoLink Data Integration Ontology Summit 2016 4 17
GeoLink Integration Architecture
Krisnadhi GeoLink Data Integration Ontology Summit 2016 5 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Motivation
Needed
Data integration providing unified view over data at different sources
Krisnadhi GeoLink Data Integration Ontology Summit 2016 2 17
DI Challenges and GeoLink Project
Challenges (regardless of architecture)
Syntactic heterogeneity different data formats serializationsSemantic heterogeneity different vocabulary different level ofgranularity in data different conceptualizationSocialnon-technical inabilityunwillingness to participate fear ofunanticipated cost worry with major changes in their local systemskeptic with scalability
GeoLink Project (wwwgeolinkorg)
Part of NSFrsquos EarthCube Program ndash one among dozens of buildingblock projectsLinked Data + Ontology design pattern-based integration
Krisnadhi GeoLink Data Integration Ontology Summit 2016 3 17
Why Ontology Patterns
Upper-level and many domain ontologies are
Hard to understand mdash too many terms too abstract too complicatedaxioms too far from real dataImpose ontological commitments that may not be acceptable by allpartiesBrittle mdash costlyhard to extend carelessly extending may cause thewhole thing breaks
Ontology design pattern (ODP) a (ldquoreusablerdquo) solution of afrequently occurring modeling problem in the domain and can act asa building block of a more complex ontology
Content pattern (CP) an ODP that models a particular genericnotion in a particular domain
Community engagement via collaborative modeling
Krisnadhi GeoLink Data Integration Ontology Summit 2016 4 17
GeoLink Integration Architecture
Krisnadhi GeoLink Data Integration Ontology Summit 2016 5 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
DI Challenges and GeoLink Project
Challenges (regardless of architecture)
Syntactic heterogeneity different data formats serializationsSemantic heterogeneity different vocabulary different level ofgranularity in data different conceptualizationSocialnon-technical inabilityunwillingness to participate fear ofunanticipated cost worry with major changes in their local systemskeptic with scalability
GeoLink Project (wwwgeolinkorg)
Part of NSFrsquos EarthCube Program ndash one among dozens of buildingblock projectsLinked Data + Ontology design pattern-based integration
Krisnadhi GeoLink Data Integration Ontology Summit 2016 3 17
Why Ontology Patterns
Upper-level and many domain ontologies are
Hard to understand mdash too many terms too abstract too complicatedaxioms too far from real dataImpose ontological commitments that may not be acceptable by allpartiesBrittle mdash costlyhard to extend carelessly extending may cause thewhole thing breaks
Ontology design pattern (ODP) a (ldquoreusablerdquo) solution of afrequently occurring modeling problem in the domain and can act asa building block of a more complex ontology
Content pattern (CP) an ODP that models a particular genericnotion in a particular domain
Community engagement via collaborative modeling
Krisnadhi GeoLink Data Integration Ontology Summit 2016 4 17
GeoLink Integration Architecture
Krisnadhi GeoLink Data Integration Ontology Summit 2016 5 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Why Ontology Patterns
Upper-level and many domain ontologies are
Hard to understand mdash too many terms too abstract too complicatedaxioms too far from real dataImpose ontological commitments that may not be acceptable by allpartiesBrittle mdash costlyhard to extend carelessly extending may cause thewhole thing breaks
Ontology design pattern (ODP) a (ldquoreusablerdquo) solution of afrequently occurring modeling problem in the domain and can act asa building block of a more complex ontology
Content pattern (CP) an ODP that models a particular genericnotion in a particular domain
Community engagement via collaborative modeling
Krisnadhi GeoLink Data Integration Ontology Summit 2016 4 17
GeoLink Integration Architecture
Krisnadhi GeoLink Data Integration Ontology Summit 2016 5 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
GeoLink Integration Architecture
Krisnadhi GeoLink Data Integration Ontology Summit 2016 5 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Modeled Notions
Content patterns corresponding to concrete domain notions
Cruise Vessel Person Organization Funding Award ProgramPhysical Sample Dataset Digital Object Publication Platform PlaceTime
Content patterns from abstraction in modeling
Agent Agent Role Event Information Object Identifier Personal InfoItem Person Name Property Value
Krisnadhi GeoLink Data Integration Ontology Summit 2016 6 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Ontology Overview
Each node represents a content pattern
Krisnadhi GeoLink Data Integration Ontology Summit 2016 7 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
CP Example Cruise
Generate competency questionsldquoFind all cruises passing through Gulf of Maine in August 2013rdquoldquoShow the tracks of cruises in operation in September 2013rdquoldquoList all cruise vessels that departed from Woods Hole in 2012rdquoldquoFind the chief scientists of any cruise that collected samples ofcarbon-isotope data in Lake SuperiorrdquoldquoWhat datasets were produced by the cruise AE0901rdquoldquoWhich cruises are funded by the NSF award DBI-0424599rdquo
Understand the nature of things we model
Cruise is an EventTrack maybe complex reuse Trajectory pattern1
Vessel maybe complexChief scientist a role of an agentDataset maybe complexFunding award maybe complex
1Hu et al ldquoA geo-ontology design pattern for semantic trajectoriesrdquo COSIT 2013
Krisnadhi GeoLink Data Integration Ontology Summit 2016 8 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Cruise CP
Krisnadhi GeoLink Data Integration Ontology Summit 2016 9 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Axiomatization
Use informal natural language to model axioms together with domainexpertsdata providers
Cruise v Event
Cruise has exactly 1 trajectory and is undertaken by exactly 1 vesselCruise v (=1 hasTrajectoryTrajectory) u (=1 isUndertakenByVessel)
Cruise is described by exactly 1 information objectCruise v (=1 isDescribedByInformationObject)
Trajectory of a cruise must be traveled by the vessel by which thecruise is undertakenhasTrajectoryminus isUndertakenBy v isTraveledBy
Krisnadhi GeoLink Data Integration Ontology Summit 2016 10 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Cruise Trajectory
Krisnadhi GeoLink Data Integration Ontology Summit 2016 11 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Bridging Semantic Heterogeneity
Since patterns represent key notions as understood by domain expertsand data providers intuitively an appropriate mappingalignmentexists between ldquolocalrdquo vocabulary and the patterns
A (local) pattern view between a data source and the patterns makessuch a mapping explicit
View is a very minimalistic schema (class names property namessimple domain and range axioms)Separating ldquocore conceptualizationrdquo and ldquonomenclaturerdquo issuesvocabulary terms in a local view may be repository-specific and neednot be the same as the patternsMapping can be expressed in rules that help populating the patternsData providers can populate the global schema (pattern collection) bysimply populating a local viewExisting controlled vocabulary can also be accommodated as a patternview
Krisnadhi GeoLink Data Integration Ontology Summit 2016 12 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Pattern View Example
Producer populates view
to populate Cruise Agent Role and Person patterns
Krisnadhi GeoLink Data Integration Ontology Summit 2016 13 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Mapping Rule
vCruise(X) and vhasChiefScientist(XY ) and vPerson(Y )
minusrarr existZ(cCruise(X) and arprovidesAgentRole(XZ)
and cChiefScientistRole(Z)
and arisPerformedBy(Z Y ) and pPerson(Y ))
Krisnadhi GeoLink Data Integration Ontology Summit 2016 14 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Mapping Rule
CONSTRUCT
X a cCruise
arprovidesAgentRole [ a cChiefScientistRole
arisPerformedBy Y ]
Y a pPerson
WHERE
X a vCruise vhasChiefScientist Y
Y a vPerson
Krisnadhi GeoLink Data Integration Ontology Summit 2016 15 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Conclusion and Outlook
The GeoLink modular oceanography ontology = collection of contentpatterns in oceanography
Collaborative modeling approach
Two-layered ontology architecture with patterns and local views helpssemantic interoperability across different data sources while allowingdata providers to retain their own local vocabulary and schema
See also httpwwwgeolinkorg httpschemageolinkorg
Krisnadhi GeoLink Data Integration Ontology Summit 2016 16 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17
Questions
Acknowledgement
NSF for GeoLink funding
Krisnadhi GeoLink Data Integration Ontology Summit 2016 17 17