Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation

eXascale Infolab
eXascale InfolabeXascale Infolab
Fixing the Domain and Range
of Properties in Linked Data
by Context Disambiguation
Alberto Tonon, Michele Catasta,
Gianluca Demartini, Philippe Cudré-Mauroux
LDOW - May the 19th, 2015
Linked Data…
2
"Cobie Smulders"
"Neil Patrick Harris"
"How I Met Your Mother"
showName
starring
starring
name
name
TV Show
type type
Person
type
type
type
type
network
type
TV Network
Broadcast
Network
type
Actor
Actor
Person
Work
… and its Schema
3
......
Thing
Person Work
TV showActor
Organisation
Broadcaster
...
Type Hierarchy
network
Broadcaster
range
domain
Broadcaster
starring
Work
range
domain
Actor
Property
Definitions
Data-Schema Coherence
4
"Cobie Smulders"
"Neil Patrick Harris"
"How I Met Your Mother"
showName
starring
starring
name
name
TV Show
type type
Person
type
type
type
type
network
type
TV Network
Broadcast
Network
type
Actor
Actor
Person
Work
network
Broadcaster
range
domain
Broadcaster
starring
Work
range
domain
Actor
Data-Schema Coherence
4
"Cobie Smulders"
"Neil Patrick Harris"
"How I Met Your Mother"
showName
starring
starring
name
name
TV Show
type type
Person
type
type
type
type
network
type
TV Network
Broadcast
Network
type
Actor
Actor
Person
Work
network
Broadcaster
range
domain
Broadcaster
starring
Work
range
domain
Actor
✔
✔
Data-Schema Coherence
4
"Cobie Smulders"
"Neil Patrick Harris"
"How I Met Your Mother"
showName
starring
starring
name
name
TV Show
type type
Person
type
type
type
type
network
type
TV Network
Broadcast
Network
type
Actor
Actor
Person
Work
network
Broadcaster
range
domain
Broadcaster
starring
Work
range
domain
Actor
✔
✔
✘
Incoherences in Real KBs
5
Property
Dom
Incoherences
% Dom
Incoherences
dpo:years ~641k 100%
dpo:currentMember ~260k 100%
… … …
Property
Dom
Incoherences
% Dom
Incoherences
fb:[…]object.type ~99M 61%
fb:[…]object.name ~41M 100%
… … …
Data-Driven
Domains/Ranges
• Just intersect the types of all resources appearing
as subject/object…
• …being consistent with the type hierarchy.
6
......
Thing
Person Work
TV showActor
Organisation
Broadcaster
...
Type Hierarchy
Data-Driven
Domains/Ranges
• Dom(foaf:name) = Thing 

—> Everything has a name !
• Dom(dpo:manager) = Thing 

—> Everything has a manager "
7
SportSeason
0.55
Agent
0.44
...
Thing
1.00
...Soccer Cricket"k 1
Rugby"k
Baseball"10.42
... ...SoccerClubSeason
0.55
SportsTeam
0.44
... ... Organisation
0.44
SportsTeamSeason
0.55
LEXT: an Example
Computing the domain of dpo:manager
8
SportSeason
0.55
Agent
0.44
...
Thing
1.00
...Soccer Cricket"k 1
Rugby"k
Baseball"10.42
... ...SoccerClubSeason
0.55
SportsTeam
0.44
... ... Organisation
0.44
SportsTeamSeason
0.55
dpo:manager is usedin two different contexts
LEXT: an Example
Computing the domain of dpo:manager
8
SportSeason
0.55
Agent
0.44
...
Thing
1.00
...Soccer Cricket"k 1
Rugby"k
Baseball"10.42
... ...SoccerClubSeason
0.55
SportsTeam
0.44
... ... Organisation
0.44
SportsTeamSeason
0.55
dpo:manager is usedin two different contexts
LEXT: an Example
Computing the domain of dpo:manager
8
manager
soccer club
season manager
sports team
manager
Thing
SoccerClubSeason SportsTeam
SportSeason
0.55
Agent
0.44
...
Thing
1.00
...Soccer Cricket"k 1
Rugby"k
Baseball"10.42
... ...SoccerClubSeason
0.55
SportsTeam
0.44
... ... Organisation
0.44
SportsTeamSeason
0.55
dpo:manager is usedin two different contexts
LEXT: an Example
Computing the domain of dpo:manager
8
manager
soccer club
season manager
sports team
manager
Thing
SoccerClubSeason SportsTeam
Visit the hierarchy until:
1) Pr(type | property) ≥ λ
&&
2) H(Pr(property | children)) < η
LEXT
H = 1.96
H = 0.9 SportSeason
0.55
Agent
0.44
...
Thing
1.00
...Soccer Cricket"k 1
Rugby"k
Baseball"10.42
... ...SoccerClubSeason
0.55
SportsTeam
0.44
... ... Organisation
0.44
SportsTeamSeason
0.55
dpo:manager is usedin two different contexts
LEXT: an Example
Computing the domain of dpo:manager
8
manager
soccer club
season manager
sports team
manager
Thing
SoccerClubSeason SportsTeam
Visit the hierarchy until:
1) Pr(type | property) ≥ λ
&&
2) H(Pr(property | children)) < η
LEXT
REXT and LERIXT
• REXT = LEXT but with types of object resources
• LERIXT = LEXT + REXT
• two type trees (one for Domain and one for
Range), current state is a pair (subject type,
object type)
9
SportSeason Agent ...
Thing
...Soccer Cricket RugbyBaseball
... ...SoccerClubSeason SportsTeam
... ... OrganisationSportsTeamSeason
SportSeason Agent ...
Thing
...Soccer Cricket RugbyBaseball
... ...SoccerClubSeason SportsTeam
... ... OrganisationSportsTeamSeason
Current State
About λ
10
Figure 1: Coverage and number of new sub-properties varying λ.
Evaluation
• Fixed λ = 0.1, η = 1
• 3 authors + 2 experts (majority vote) evaluated the
output of LEXT REXT, and LERIXT.
• LERIXT generates too many new sub-properties
11
LEXT REXT LERIXT
Precision 96.50% 91.40% 87.00%
Table 2: Precision of LEXT, REXT, and LERIXT
Conclusion
• Three different methods for identifying contexts
• LEXT: exploits the type of the subject resources
• REXT: exploits the type of the object resources
• LERIXT: exploits both
• Up to 96.50% precision.
12
Visit the hierarchy until:
1) Pr(type | property) ≥ λ
&&
2) H(Pr(property | children)) < η
LEXT
1 of 18

Recommended

TRank ISWC2013 by
TRank ISWC2013TRank ISWC2013
TRank ISWC2013eXascale Infolab
2.6K views27 slides
Combining Inverted Indices and Structured Search for Ad-hoc Object Retrieval by
Combining Inverted Indices and Structured Search for  Ad-hoc Object RetrievalCombining Inverted Indices and Structured Search for  Ad-hoc Object Retrieval
Combining Inverted Indices and Structured Search for Ad-hoc Object RetrievaleXascale Infolab
2.7K views20 slides
Efficient Parallel Set-Similarity Joins Using Hadoop__HadoopSummit2010 by
Efficient Parallel Set-Similarity Joins Using Hadoop__HadoopSummit2010Efficient Parallel Set-Similarity Joins Using Hadoop__HadoopSummit2010
Efficient Parallel Set-Similarity Joins Using Hadoop__HadoopSummit2010Yahoo Developer Network
1.5K views30 slides
Empirical Semantics by
Empirical SemanticsEmpirical Semantics
Empirical SemanticsFrank van Harmelen
3K views44 slides
Walking Linked Data: a graph traversal approach to explain clusters by
Walking Linked Data: a graph traversal approach to explain clustersWalking Linked Data: a graph traversal approach to explain clusters
Walking Linked Data: a graph traversal approach to explain clustersVrije Universiteit Amsterdam
1.2K views22 slides
Building a Mongo DSL in Scala at Hot Potato by
Building a Mongo DSL in Scala at Hot PotatoBuilding a Mongo DSL in Scala at Hot Potato
Building a Mongo DSL in Scala at Hot PotatoMongoDB
714 views13 slides

More Related Content

Similar to Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation

Recommender Systems, Matrices and Graphs by
Recommender Systems, Matrices and GraphsRecommender Systems, Matrices and Graphs
Recommender Systems, Matrices and GraphsRoelof Pieters
6.7K views167 slides
Types Working for You, Not Against You by
Types Working for You, Not Against YouTypes Working for You, Not Against You
Types Working for You, Not Against YouC4Media
277 views124 slides
Introducción a Neo4j by
Introducción a Neo4jIntroducción a Neo4j
Introducción a Neo4jNeo4j
32 views27 slides
R for Pythonistas (PyData NYC 2017) by
R for Pythonistas (PyData NYC 2017)R for Pythonistas (PyData NYC 2017)
R for Pythonistas (PyData NYC 2017)Christopher Roach
776 views27 slides
Amazon DynamoDB Design Workshop by
Amazon DynamoDB Design WorkshopAmazon DynamoDB Design Workshop
Amazon DynamoDB Design WorkshopAmazon Web Services
1.5K views34 slides
Breaking down data silos with the open data protocol by
Breaking down data silos with the open data protocolBreaking down data silos with the open data protocol
Breaking down data silos with the open data protocolWoodruff Solutions LLC
924 views30 slides

Similar to Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation (11)

Recommender Systems, Matrices and Graphs by Roelof Pieters
Recommender Systems, Matrices and GraphsRecommender Systems, Matrices and Graphs
Recommender Systems, Matrices and Graphs
Roelof Pieters6.7K views
Types Working for You, Not Against You by C4Media
Types Working for You, Not Against YouTypes Working for You, Not Against You
Types Working for You, Not Against You
C4Media277 views
Introducción a Neo4j by Neo4j
Introducción a Neo4jIntroducción a Neo4j
Introducción a Neo4j
Neo4j32 views
Colour Modelling - domain modelling with the 3rd dimension by Douglas English
Colour Modelling - domain modelling with the 3rd dimensionColour Modelling - domain modelling with the 3rd dimension
Colour Modelling - domain modelling with the 3rd dimension
Douglas English1.1K views
Mining Interesting Trivia for Entities from Wikipedia PART-II by Abhay Prakash
Mining Interesting Trivia for Entities from Wikipedia PART-IIMining Interesting Trivia for Entities from Wikipedia PART-II
Mining Interesting Trivia for Entities from Wikipedia PART-II
Abhay Prakash554 views
Looking at Content Recommendations through a Search Lens - Extended Version by Sonya Liberman
Looking at Content Recommendations through a Search Lens - Extended VersionLooking at Content Recommendations through a Search Lens - Extended Version
Looking at Content Recommendations through a Search Lens - Extended Version
Sonya Liberman128 views

More from eXascale Infolab

Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction by
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link PredictionBeyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link PredictioneXascale Infolab
287 views30 slides
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S... by
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...eXascale Infolab
167 views16 slides
Representation Learning on Complex Graphs by
Representation Learning on Complex GraphsRepresentation Learning on Complex Graphs
Representation Learning on Complex GraphseXascale Infolab
539 views33 slides
A force directed approach for offline gps trajectory map by
A force directed approach for offline gps trajectory mapA force directed approach for offline gps trajectory map
A force directed approach for offline gps trajectory mapeXascale Infolab
459 views12 slides
Cikm 2018 by
Cikm 2018Cikm 2018
Cikm 2018eXascale Infolab
871 views18 slides
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit... by
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...eXascale Infolab
787 views20 slides

More from eXascale Infolab(20)

Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction by eXascale Infolab
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link PredictionBeyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction
eXascale Infolab287 views
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S... by eXascale Infolab
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...
It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...
eXascale Infolab167 views
Representation Learning on Complex Graphs by eXascale Infolab
Representation Learning on Complex GraphsRepresentation Learning on Complex Graphs
Representation Learning on Complex Graphs
eXascale Infolab539 views
A force directed approach for offline gps trajectory map by eXascale Infolab
A force directed approach for offline gps trajectory mapA force directed approach for offline gps trajectory map
A force directed approach for offline gps trajectory map
eXascale Infolab459 views
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit... by eXascale Infolab
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...
eXascale Infolab787 views
SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous... by eXascale Infolab
SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous...SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous...
SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous...
eXascale Infolab1.2K views
Dependency-Driven Analytics: A Compass for Uncharted Data Oceans by eXascale Infolab
Dependency-Driven Analytics: A Compass for Uncharted Data OceansDependency-Driven Analytics: A Compass for Uncharted Data Oceans
Dependency-Driven Analytics: A Compass for Uncharted Data Oceans
eXascale Infolab687 views
SANAPHOR: Ontology-based Coreference Resolution by eXascale Infolab
SANAPHOR: Ontology-based Coreference ResolutionSANAPHOR: Ontology-based Coreference Resolution
SANAPHOR: Ontology-based Coreference Resolution
eXascale Infolab1.1K views
Efficient, Scalable, and Provenance-Aware Management of Linked Data by eXascale Infolab
Efficient, Scalable, and Provenance-Aware Management of Linked DataEfficient, Scalable, and Provenance-Aware Management of Linked Data
Efficient, Scalable, and Provenance-Aware Management of Linked Data
eXascale Infolab713 views
LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data by eXascale Infolab
LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked DataLDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data
LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data
eXascale Infolab4K views
Executing Provenance-Enabled Queries over Web Data by eXascale Infolab
Executing Provenance-Enabled Queries over Web DataExecuting Provenance-Enabled Queries over Web Data
Executing Provenance-Enabled Queries over Web Data
eXascale Infolab1.5K views
The Dynamics of Micro-Task Crowdsourcing by eXascale Infolab
The Dynamics of Micro-Task CrowdsourcingThe Dynamics of Micro-Task Crowdsourcing
The Dynamics of Micro-Task Crowdsourcing
eXascale Infolab1.6K views
CIKM14: Fixing grammatical errors by preposition ranking by eXascale Infolab
CIKM14: Fixing grammatical errors by preposition rankingCIKM14: Fixing grammatical errors by preposition ranking
CIKM14: Fixing grammatical errors by preposition ranking
eXascale Infolab1.7K views
Internet Infrastructures for Big Data (Verisign's Distinguished Speaker Series) by eXascale Infolab
Internet Infrastructures for Big Data (Verisign's Distinguished Speaker Series)Internet Infrastructures for Big Data (Verisign's Distinguished Speaker Series)
Internet Infrastructures for Big Data (Verisign's Distinguished Speaker Series)
eXascale Infolab663 views

Recently uploaded

Connecting communities to promote FAIR resources: perspectives from an RDA / ... by
Connecting communities to promote FAIR resources: perspectives from an RDA / ...Connecting communities to promote FAIR resources: perspectives from an RDA / ...
Connecting communities to promote FAIR resources: perspectives from an RDA / ...Allyson Lister
33 views49 slides
RemeOs science and clinical evidence by
RemeOs science and clinical evidenceRemeOs science and clinical evidence
RemeOs science and clinical evidencePetrusViitanen1
26 views96 slides
plasmids by
plasmidsplasmids
plasmidsscribddarkened352
7 views2 slides
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance... by
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...InsideScientific
9 views62 slides
MILK LIPIDS 2.pptx by
MILK LIPIDS 2.pptxMILK LIPIDS 2.pptx
MILK LIPIDS 2.pptxabhinambroze18
7 views15 slides
Experimental animal Guinea pigs.pptx by
Experimental animal Guinea pigs.pptxExperimental animal Guinea pigs.pptx
Experimental animal Guinea pigs.pptxMansee Arya
10 views16 slides

Recently uploaded(20)

Connecting communities to promote FAIR resources: perspectives from an RDA / ... by Allyson Lister
Connecting communities to promote FAIR resources: perspectives from an RDA / ...Connecting communities to promote FAIR resources: perspectives from an RDA / ...
Connecting communities to promote FAIR resources: perspectives from an RDA / ...
Allyson Lister33 views
RemeOs science and clinical evidence by PetrusViitanen1
RemeOs science and clinical evidenceRemeOs science and clinical evidence
RemeOs science and clinical evidence
PetrusViitanen126 views
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance... by InsideScientific
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...
A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cance...
Experimental animal Guinea pigs.pptx by Mansee Arya
Experimental animal Guinea pigs.pptxExperimental animal Guinea pigs.pptx
Experimental animal Guinea pigs.pptx
Mansee Arya10 views
Conventional and non-conventional methods for improvement of cucurbits.pptx by gandhi976
Conventional and non-conventional methods for improvement of cucurbits.pptxConventional and non-conventional methods for improvement of cucurbits.pptx
Conventional and non-conventional methods for improvement of cucurbits.pptx
gandhi97616 views
A training, certification and marketing scheme for informal dairy vendors in ... by ILRI
A training, certification and marketing scheme for informal dairy vendors in ...A training, certification and marketing scheme for informal dairy vendors in ...
A training, certification and marketing scheme for informal dairy vendors in ...
ILRI10 views
Artificial Intelligence Helps in Drug Designing and Discovery.pptx by abhinashsahoo2001
Artificial Intelligence Helps in Drug Designing and Discovery.pptxArtificial Intelligence Helps in Drug Designing and Discovery.pptx
Artificial Intelligence Helps in Drug Designing and Discovery.pptx
abhinashsahoo2001117 views
Open Access Publishing in Astrophysics by Peter Coles
Open Access Publishing in AstrophysicsOpen Access Publishing in Astrophysics
Open Access Publishing in Astrophysics
Peter Coles543 views
Pollination By Nagapradheesh.M.pptx by MNAGAPRADHEESH
Pollination By Nagapradheesh.M.pptxPollination By Nagapradheesh.M.pptx
Pollination By Nagapradheesh.M.pptx
MNAGAPRADHEESH15 views
MODULE-9-Biotechnology, Genetically Modified Organisms, and Gene Therapy.pdf by KerryNuez1
MODULE-9-Biotechnology, Genetically Modified Organisms, and Gene Therapy.pdfMODULE-9-Biotechnology, Genetically Modified Organisms, and Gene Therapy.pdf
MODULE-9-Biotechnology, Genetically Modified Organisms, and Gene Therapy.pdf
KerryNuez121 views
himalay baruah acid fast staining.pptx by HimalayBaruah
himalay baruah acid fast staining.pptxhimalay baruah acid fast staining.pptx
himalay baruah acid fast staining.pptx
HimalayBaruah5 views
application of genetic engineering 2.pptx by SankSurezz
application of genetic engineering 2.pptxapplication of genetic engineering 2.pptx
application of genetic engineering 2.pptx
SankSurezz6 views

Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation

  • 1. Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation Alberto Tonon, Michele Catasta, Gianluca Demartini, Philippe Cudré-Mauroux LDOW - May the 19th, 2015
  • 2. Linked Data… 2 "Cobie Smulders" "Neil Patrick Harris" "How I Met Your Mother" showName starring starring name name TV Show type type Person type type type type network type TV Network Broadcast Network type Actor Actor Person Work
  • 3. … and its Schema 3 ...... Thing Person Work TV showActor Organisation Broadcaster ... Type Hierarchy network Broadcaster range domain Broadcaster starring Work range domain Actor Property Definitions
  • 4. Data-Schema Coherence 4 "Cobie Smulders" "Neil Patrick Harris" "How I Met Your Mother" showName starring starring name name TV Show type type Person type type type type network type TV Network Broadcast Network type Actor Actor Person Work network Broadcaster range domain Broadcaster starring Work range domain Actor
  • 5. Data-Schema Coherence 4 "Cobie Smulders" "Neil Patrick Harris" "How I Met Your Mother" showName starring starring name name TV Show type type Person type type type type network type TV Network Broadcast Network type Actor Actor Person Work network Broadcaster range domain Broadcaster starring Work range domain Actor ✔ ✔
  • 6. Data-Schema Coherence 4 "Cobie Smulders" "Neil Patrick Harris" "How I Met Your Mother" showName starring starring name name TV Show type type Person type type type type network type TV Network Broadcast Network type Actor Actor Person Work network Broadcaster range domain Broadcaster starring Work range domain Actor ✔ ✔ ✘
  • 7. Incoherences in Real KBs 5 Property Dom Incoherences % Dom Incoherences dpo:years ~641k 100% dpo:currentMember ~260k 100% … … … Property Dom Incoherences % Dom Incoherences fb:[…]object.type ~99M 61% fb:[…]object.name ~41M 100% … … …
  • 8. Data-Driven Domains/Ranges • Just intersect the types of all resources appearing as subject/object… • …being consistent with the type hierarchy. 6 ...... Thing Person Work TV showActor Organisation Broadcaster ... Type Hierarchy
  • 9. Data-Driven Domains/Ranges • Dom(foaf:name) = Thing 
 —> Everything has a name ! • Dom(dpo:manager) = Thing 
 —> Everything has a manager " 7
  • 10. SportSeason 0.55 Agent 0.44 ... Thing 1.00 ...Soccer Cricket"k 1 Rugby"k Baseball"10.42 ... ...SoccerClubSeason 0.55 SportsTeam 0.44 ... ... Organisation 0.44 SportsTeamSeason 0.55 LEXT: an Example Computing the domain of dpo:manager 8
  • 11. SportSeason 0.55 Agent 0.44 ... Thing 1.00 ...Soccer Cricket"k 1 Rugby"k Baseball"10.42 ... ...SoccerClubSeason 0.55 SportsTeam 0.44 ... ... Organisation 0.44 SportsTeamSeason 0.55 dpo:manager is usedin two different contexts LEXT: an Example Computing the domain of dpo:manager 8
  • 12. SportSeason 0.55 Agent 0.44 ... Thing 1.00 ...Soccer Cricket"k 1 Rugby"k Baseball"10.42 ... ...SoccerClubSeason 0.55 SportsTeam 0.44 ... ... Organisation 0.44 SportsTeamSeason 0.55 dpo:manager is usedin two different contexts LEXT: an Example Computing the domain of dpo:manager 8 manager soccer club season manager sports team manager Thing SoccerClubSeason SportsTeam
  • 13. SportSeason 0.55 Agent 0.44 ... Thing 1.00 ...Soccer Cricket"k 1 Rugby"k Baseball"10.42 ... ...SoccerClubSeason 0.55 SportsTeam 0.44 ... ... Organisation 0.44 SportsTeamSeason 0.55 dpo:manager is usedin two different contexts LEXT: an Example Computing the domain of dpo:manager 8 manager soccer club season manager sports team manager Thing SoccerClubSeason SportsTeam Visit the hierarchy until: 1) Pr(type | property) ≥ λ && 2) H(Pr(property | children)) < η LEXT
  • 14. H = 1.96 H = 0.9 SportSeason 0.55 Agent 0.44 ... Thing 1.00 ...Soccer Cricket"k 1 Rugby"k Baseball"10.42 ... ...SoccerClubSeason 0.55 SportsTeam 0.44 ... ... Organisation 0.44 SportsTeamSeason 0.55 dpo:manager is usedin two different contexts LEXT: an Example Computing the domain of dpo:manager 8 manager soccer club season manager sports team manager Thing SoccerClubSeason SportsTeam Visit the hierarchy until: 1) Pr(type | property) ≥ λ && 2) H(Pr(property | children)) < η LEXT
  • 15. REXT and LERIXT • REXT = LEXT but with types of object resources • LERIXT = LEXT + REXT • two type trees (one for Domain and one for Range), current state is a pair (subject type, object type) 9 SportSeason Agent ... Thing ...Soccer Cricket RugbyBaseball ... ...SoccerClubSeason SportsTeam ... ... OrganisationSportsTeamSeason SportSeason Agent ... Thing ...Soccer Cricket RugbyBaseball ... ...SoccerClubSeason SportsTeam ... ... OrganisationSportsTeamSeason Current State
  • 16. About λ 10 Figure 1: Coverage and number of new sub-properties varying λ.
  • 17. Evaluation • Fixed λ = 0.1, η = 1 • 3 authors + 2 experts (majority vote) evaluated the output of LEXT REXT, and LERIXT. • LERIXT generates too many new sub-properties 11 LEXT REXT LERIXT Precision 96.50% 91.40% 87.00% Table 2: Precision of LEXT, REXT, and LERIXT
  • 18. Conclusion • Three different methods for identifying contexts • LEXT: exploits the type of the subject resources • REXT: exploits the type of the object resources • LERIXT: exploits both • Up to 96.50% precision. 12 Visit the hierarchy until: 1) Pr(type | property) ≥ λ && 2) H(Pr(property | children)) < η LEXT