si-ls 2008-09 - group 15 - sorbini, savioli, reale - presentation slides
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Leveraging Data and Structure inOntology IntegrationO. Udrea L. Getoor R.J. Miller
Group 15Enrico Savioli Andrea Reale Andrea Sorbini
DEISUniversity of Bologna
Searching Information in Large Spaces Conference
March 11, 2009
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Outline
1 Brief Introduction to Ontologies
What is an Ontology
2 Motivations and State of the Art
Bringing data together
Existing Solutions
3 ILIADS
Overview
The Algorithm
Experimental ResultsConclusions and Future Developments
4 Demo
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa quest for meaning
A very common problem in IT is data modeling Critical in the field of Information Systems A good data model enables good data usage
Tools used to describe concepts cannot expressthe implicit
interpretation of data
Semantic knowledge is lost after the modeling process
Example
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa quest for meaning
A very common problem in IT is data modeling Critical in the field of Information Systems A good data model enables good data usage
Tools used to describe concepts cannot expressthe implicit
interpretation of data
Semantic knowledge is lost after the modeling process
Example
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 3 / 39
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa quest for meaning
A very common problem in IT is data modeling Critical in the field of Information Systems A good data model enables good data usage
Tools used to describe concepts cannot expressthe implicit
interpretation of data
Semantic knowledge is lost after the modeling process
Example
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B i f I d i O l i Wh i O l
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa quest for meaning
A very common problem in IT is data modeling Critical in the field of Information Systems A good data model enables good data usage
Tools used to describe concepts cannot expressthe implicit
interpretation of data
Semantic knowledge is lost after the modeling process
Example
How can we find all of Queens members?
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B i f I t d ti t O t l i Wh t i O t l
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa formal model
Ontology
An ontology is a formal representationof
concepts within a domain (Song, Performer, Composer...)
relations between those concepts (plays-for, member-of, written-by...)individual instances (queen, f.mercury, b.may...)
Well-defined constructsto enrich descriptions with semantics
Formal models to enable automatic reasoning(DL, FOL)
(member(Y,Z) :- sings-for(Y,Z) ; plays-for(Y,Z))
OWL is the W3C standard for an ontology language
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Brief Introduction to Ontologies What is an Ontology
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa formal model
Ontology
An ontology is a formal representationof
concepts within a domain (Song, Performer, Composer...)
relations between those concepts (plays-for, member-of, written-by...)individual instances (queen, f.mercury, b.may...)
Well-defined constructsto enrich descriptions with semantics
Formal models to enable automatic reasoning(DL, FOL)
(member(Y,Z) :- sings-for(Y,Z) ; plays-for(Y,Z))
OWL is the W3C standard for an ontology language
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 4 / 39
Brief Introduction to Ontologies What is an Ontology
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Brief Introduction to Ontologies What is an Ontology
Ontologiesa formal model
Ontology
An ontology is a formal representationof
concepts within a domain (Song, Performer, Composer...)
relations between those concepts (plays-for, member-of, written-by...)individual instances (queen, f.mercury, b.may...)
Well-defined constructsto enrich descriptions with semantics
Formal models to enable automatic reasoning(DL, FOL)
(member(Y,Z) :- sings-for(Y,Z) ; plays-for(Y,Z))
OWL is the W3C standard for an ontology language
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 4 / 39
Brief Introduction to Ontologies What is an Ontology
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Brief Introduction to Ontologies What is an Ontology
OWLA simple example
Whole lottalove
subClassOf subClassOfsubClassOf
Musician Writer
Iron MaidenVienna
PhilharmonicS. Harris
subClassOfsubClassOf
type typetype
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
typetype
type
JazzMusic
type
RockMusic
W.A. Mozart
composer-ofplays
Performer
performs
composer-of
Smoke onthe water
type
Music
type
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Motivations and State of the Art Bringing data together
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Motivations and State of the Art Bringing data together
Motivating Scenario
The AAA principleAnyonecan sayAnythingaboutAny topic
The same thing might be described in different ways
There is a strong need to integrate heterogeneous schemas Not only in a web scenario
Example
DB schemas
XML schemas
Ontology schemas
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Motivations and State of the Art Bringing data together
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Motivations and State of the Art Bringing data together
Motivating Scenario
The AAA principleAnyonecan sayAnythingaboutAny topic
The same thing might be described in different ways
There is a strong need to integrate heterogeneous schemas Not only in a web scenario
Example
DB schemas
XML schemas
Ontology schemas
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Motivations and State of the Art Bringing data together
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g g g
Motivating Scenario
The AAA principleAnyonecan sayAnythingaboutAny topic
The same thing might be described in different ways
There is a strong need to integrate heterogeneous schemas Not only in a web scenario
Example
DB schemas
XML schemas
Ontology schemas
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Motivations and State of the Art Bringing data together
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Integration Techniques
Structure Based Matching
Uses schema meta-data(e.g.
informations about tables orconcepts models) to discover
mapping elements among
them, both on a structural and
element level
Instance Based Matching
Uses informations about data
instances(e.g. contents of atable or individuals of an
ontology) to discover mappings
among entities representing
them
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Motivations and State of the Art Bringing data together
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Integration Techniques
Structure Based Matching
Uses schema meta-data(e.g.
informations about tables orconcepts models) to discover
mapping elements among
them, both on a structural and
element level
Instance Based Matching
Uses informations about data
instances(e.g. contents of atable or individuals of an
ontology) to discover mappings
among entities representing
them
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Motivations and State of the Art Bringing data together
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Ontology Integration
Ontologies offer further ways to uncover aligning relations
between entities
Explicit theoretic model semantics can be leveraged to improve
integration quality
ExampleAssume that composed-by is a functional property
Requiem K626and Requiem in D minorare the same
If we have:
composed-by(Requiem K626, Mozart). composed-by(Requiem in D minor, W.A. Mozart).
Then we might say that aligning W.A. Mozartand Mozart is a
good choice
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Motivations and State of the Art Bringing data together
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Ontology Integration
Ontologies offer further ways to uncover aligning relations
between entities
Explicit theoretic model semantics can be leveraged to improve
integration quality
ExampleAssume that composed-by is a functional property
Requiem K626and Requiem in D minorare the same
If we have:
composed-by(Requiem K626, Mozart). composed-by(Requiem in D minor, W.A. Mozart).
Then we might say that aligning W.A. Mozartand Mozart is a
good choice
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 9 / 39
Motivations and State of the Art Bringing data together
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Ontology Integration
Ontologies offer further ways to uncover aligning relations
between entities
Explicit theoretic model semantics can be leveraged to improve
integration quality
ExampleAssume that composed-by is a functional property
Requiem K626and Requiem in D minorare the same
If we have:
composed-by(Requiem K626, Mozart). composed-by(Requiem in D minor, W.A. Mozart).
Then we might say that aligning W.A. Mozartand Mozart is a
good choice
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 9 / 39
Motivations and State of the Art Bringing data together
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Ontology Integration
Ontologies offer further ways to uncover aligning relations
between entities
Explicit theoretic model semantics can be leveraged to improve
integration quality
ExampleAssume that composed-by is a functional property
Requiem K626and Requiem in D minorare the same
If we have:
composed-by(Requiem K626, Mozart). composed-by(Requiem in D minor, W.A. Mozart).
Then we might say that aligning W.A. Mozartand Mozart is a
good choice
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 9 / 39
Motivations and State of the Art Bringing data together
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Ontology Integration
Ontologies offer further ways to uncover aligning relations
between entities
Explicit theoretic model semantics can be leveraged to improve
integration quality
ExampleAssume that composed-by is a functional property
Requiem K626and Requiem in D minorare the same
If we have:
composed-by(Requiem K626, Mozart). composed-by(Requiem in D minor, W.A. Mozart).
Then we might say that aligning W.A. Mozartand Mozart is a
good choice
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 9 / 39
Motivations and State of the Art Existing Solutions
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FCA-Merge and COMA++
FCA-Merge (human-aided tool to merge ontologies)
Collects domain related natural language documents
Searches those documents for ontologies concepts occurrences
Derives an alignment that has to bevalidated by an operator
COMA++ (framework with multiple match strategies)
Fragment based matching
Reuse of previous matching results
Comprehensive GUI for results evaluation
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Motivations and State of the Art Existing Solutions
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FCA-Merge and COMA++
FCA-Merge (human-aided tool to merge ontologies)
Collects domain related natural language documents
Searches those documents for ontologies concepts occurrences
Derives an alignment that has to bevalidated by an operator
COMA++ (framework with multiple match strategies)
Fragment based matching
Reuse of previous matching results
Comprehensive GUI for results evaluation
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 10 / 39
Motivations and State of the Art Existing Solutions
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Whats missing
Both COMA++ and FCA-Merge use only structure level matching
Moreover none of them makes use of the semantic potential
offered by ontologies
There exist other solutions using reasoning support, however ... it is used only for an a-posteriori consistency checkof the result
An interesting improvement might be to leverage it for the actual
matching process
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 11 / 39
Motivations and State of the Art Existing Solutions
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Whats missing
Both COMA++ and FCA-Merge use only structure level matching
Moreover none of them makes use of the semantic potential
offered by ontologies
There exist other solutions using reasoning support, however ... it is used only for an a-posteriori consistency checkof the result
An interesting improvement might be to leverage it for the actual
matching process
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 11 / 39
Motivations and State of the Art Existing Solutions
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Whats missing
Both COMA++ and FCA-Merge use only structure level matching
Moreover none of them makes use of the semantic potential
offered by ontologies
There exist other solutions using reasoning support, however ... it is used only for an a-posteriori consistency checkof the result
An interesting improvement might be to leverage it for the actual
matching process
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 11 / 39
Motivations and State of the Art Existing Solutions
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Whats missing
Both COMA++ and FCA-Merge use only structure level matching
Moreover none of them makes use of the semantic potential
offered by ontologies
There exist other solutions using reasoning support, however ... it is used only for an a-posteriori consistency checkof the result
An interesting improvement might be to leverage it for the actual
matching process
Thats where ILIADS kicks in!
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ILIADS Overview
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ILIADSIntegrated Learning In Alignment of Data and Schema
Takes two OWL Lite ontologies as input
Combines"traditional" schema matching approaches with alogical inference algorithm Inference results are used to influence confidence in a presumed
mapping
Makes use of both schema (structure) and data (individuals)
Outputsa set of axioms (the alignment) that tights the input
ontologies together
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ILIADS Overview
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ILIADSIntegrated Learning In Alignment of Data and Schema
Takes two OWL Lite ontologies as input
Combines"traditional" schema matching approaches with alogical inference algorithm Inference results are used to influence confidence in a presumed
mapping
Makes use of both schema (structure) and data (individuals)
Outputsa set of axioms (the alignment) that tights the input
ontologies together
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 12 / 39
ILIADS Overview
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ILIADSIntegrated Learning In Alignment of Data and Schema
Takes two OWL Lite ontologies as input
Combines"traditional" schema matching approaches with alogical inference algorithm Inference results are used to influence confidence in a presumed
mapping
Makes use of both schema (structure) and data (individuals)
Outputsa set of axioms (the alignment) that tights the input
ontologies together
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ILIADS Overview
ILIADS
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ILIADSIntegrated Learning In Alignment of Data and Schema
Takes two OWL Lite ontologies as input
Combines"traditional" schema matching approaches with alogical inference algorithm Inference results are used to influence confidence in a presumed
mapping
Makes use of both schema (structure) and data (individuals)
Outputsa set of axioms (the alignment) that tights the input
ontologies together
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ILIADS The Algorithm
Al ith O i
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Algorithm Overview
INPUT: Consistent Ontologies O1 and O2OUTPUT: Alignment A
01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters05: for each couple (c, c) of that type do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t11: return A
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ILIADS The Algorithm
Th Al ith
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The AlgorithmStep by step
01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters
05: for each couple (c, c) of that type do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t11: return A
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ILIADS The Algorithm
Th Al ith
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The AlgorithmStructures initialization
The algorithms groups equivalent entities in clusters
Clusters are classified by the type of their entities Clusters of Classes
Clusters of Properties Clusters of Individuals
A new alignment can result in Merging of clusters A new subsumption relationship between clusters
At the beginning a cluster is created for each entity
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ILIADS The Algorithm
The Algorithm
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The AlgorithmStructures initialization
The algorithms groups equivalent entities in clusters
Clusters are classified by the type of their entities Clusters of Classes
Clusters of Properties Clusters of Individuals
A new alignment can result in Merging of clusters A new subsumption relationship between clusters
At the beginning a cluster is created for each entity
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ILIADS The Algorithm
The Algorithm
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The AlgorithmStructures initialization
The algorithms groups equivalent entities in clusters
Clusters are classified by the type of their entities Clusters of Classes
Clusters of Properties Clusters of Individuals
A new alignment can result in Merging of clusters A new subsumption relationship between clusters
At the beginning a cluster is created for each entity
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ILIADS The Algorithm
The Algorithm
-
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The AlgorithmSimilarity Computation
Entities similarity score
sim(e, e) = x simlex(e, e) + s simstruct(e, e
) + e simext(e, e)
lexical: Jaro-Winkler (similar to edit distance) and thesauri
structural: Jaccard for neighborhoods (Jacd(S1, S2) =|S1S2||S1S2|
)
extensional: Jaccard on extensions
The set of parameters {x, s, e} is different for each entity type
Similarity between clusters is computed combining the similarities
of their entities
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ILIADS The Algorithm
The Algorithm
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The AlgorithmSimilarity Computation
Entities similarity score
sim(e, e) = x simlex(e, e) + s simstruct(e, e
) + e simext(e, e)
lexical: Jaro-Winkler (similar to edit distance) and thesauri
structural: Jaccard for neighborhoods (Jacd(S1, S2) =|S1S2||S1S2|
)
extensional: Jaccard on extensions
The set of parameters {x, s, e} is different for each entity type
Similarity between clusters is computed combining the similarities
of their entities
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ILIADS The Algorithm
The Algorithm
-
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The AlgorithmStep by step
01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters
05: for each couple (c, c
) of that type do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t11: return A
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ILIADS The Algorithm
The Algorithm
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The AlgorithmSelecting a relationship
Iterates over each couple (c, c) such that sim(c, c) is above athreshold t
For each of those a candidate relationship is chosen between: Equivalence Subsumption
The selection is done by looking at the intersection of entitiesextensions:
The set of its instance individuals, for a class The couples of individuals involved, for a property
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ILIADS The Algorithm
The Algorithm
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The AlgorithmSelecting a relationship
Iterates over each couple (c, c) such that sim(c, c) is above athreshold t
For each of those a candidate relationship is chosen between: Equivalence Subsumption
The selection is done by looking at the intersection of entitiesextensions:
The set of its instance individuals, for a class The couples of individuals involved, for a property
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 20 / 39
ILIADS The Algorithm
The Algorithm
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The AlgorithmSelecting a relationship
Iterates over each couple (c, c) such that sim(c, c) is above athreshold t
For each of those a candidate relationship is chosen between: Equivalence Subsumption
The selection is done by looking at the intersection of entitiesextensions:
The set of its instance individuals, for a class The couples of individuals involved, for a property
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 20 / 39
ILIADS The Algorithm
The Algorithm
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The AlgorithmSelecting a relationship - Example
MusicArtist
The BlackSabbath
W.A. Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
composer-of
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem in
D minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
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ILIADS The Algorithm
The Algorithm
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e go tSelecting a relationship - Example
MusicArtist
The BlackSabbath
W.A. Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
composer-of
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem in
D minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
sameAs
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ILIADS The Algorithm
The Algorithm
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gSelecting a relationship - Example
MusicArtist
The BlackSabbath
W.A. Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
composer-of
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem in
D minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
sameAs
Number of instances belongingonly to "Rock Music" over the
total number of instancesconsidered:
2/3
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ILIADS The Algorithm
The Algorithm
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gSelecting a relationship - Example
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ILIADS The Algorithm
The Algorithm
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gSelecting a relationship - Example
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ILIADS The Algorithm
The Algorithm
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gStep by step
01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters
05: for each couple (c, c
) of that type do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t
11: return A
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ILIADS The Algorithm
The Algorithm
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Incremental Logical Inference
The inference step is used to: Look for inconsistencies of the candidate relationship Infer logical consequences of the new axiom Possibly enforce the confidence in it
The inference is not complete(it would be EXPTIME in OWL Lite) Only a small number of steps is actually performed However this may cause inconsistencies not to be found
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The Algorithm
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Incremental Logical Inference
The inference step is used to: Look for inconsistencies of the candidate relationship Infer logical consequences of the new axiom Possibly enforce the confidence in it
The inference is not complete(it would be EXPTIME in OWL Lite) Only a small number of steps is actually performed However this may cause inconsistencies not to be found
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The Algorithm
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Incremental Logical Inference
The inference step is used to: Look for inconsistencies of the candidate relationship Infer logical consequences of the new axiom Possibly enforce the confidence in it
The inference is not complete(it would be EXPTIME in OWL Lite) Only a small number of steps is actually performed However this may cause inconsistencies not to be found
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ILIADS The Algorithm
The Algorithm
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Incremental Logical Inference
The inference step is used to: Look for inconsistencies of the candidate relationship Infer logical consequences of the new axiom
Possibly enforce the confidence in it
The inference is not complete(it would be EXPTIME in OWL Lite) Only a small number of steps is actually performed However this may cause inconsistencies not to be found
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The Algorithm
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Incremental Logical Inference - Example
MusicArtist
The BlackSabbath
Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
composed-by
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ILIADS The Algorithm
The Algorithm
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Incremental Logical Inference - Example
MusicArtist
The BlackSabbath
Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
sameAs
composed-by
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ILIADS The Algorithm
The AlgorithmI l L i l I f E l
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Incremental Logical Inference - Example
MusicArtist
The BlackSabbath
Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
sameAs
composed-by
composed-by
inverseOf
composer-of
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ILIADS The Algorithm
The AlgorithmI t l L i l I f E l
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Incremental Logical Inference - Example
MusicArtist
The BlackSabbath
Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
sameAs
composed-by
composed-by
composer-of
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-
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ILIADS The Algorithm
The AlgorithmUpdate similarity score
-
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Update similarity score
This is the key point of the algorithm A value f - the influence factorof the inference - is computed
The f factor
f =
(e1,e2)Q
sim(e1, e2)1 sim(e1, e2)
Q: the set of entity pairs that became equivalent as a consequence of inference
f is used to update the similarity score for the current couple siminf(c, c
) = min(f sim(c, c), 1)
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ILIADS The Algorithm
The AlgorithmUpdate similarity score
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Update similarity score
This is the key point of the algorithm A value f - the influence factorof the inference - is computed
The f factor
f =
(e1,e2)Q
sim(e1, e2)1 sim(e1, e2)
Q: the set of entity pairs that became equivalent as a consequence of inference
f is used to update the similarity score for the current couple siminf(c, c
) = min(f sim(c, c), 1)
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ILIADS The Algorithm
The AlgorithmUpdate similarity score - Example
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Update similarity score Example
MusicArtist
The BlackSabbath
Mozart
Music Piece
Requiem K626
Smoke onthe water
subClassOf
type
type
ClassicRock
type
subClassOfMetal
subClassOf
Fusion
Quadrant Four
Whole lottalove
Paranoid
Orchestral
subClassOf
type
plays
type
type
type
type
subClassOf subClassOf
subClassOf
MusicianWriter
Iron Maiden
ViennaPhilharmonic
S. Harris
subClassOf
subClassOf
type
type
type
type
ClassicalMusic
Ride of theValkyries
Requiem inD minor
Take the "A"Train
The Phantomof the Opera
type
type
type
Jazz
Music
type
RockMusic
W.A. Mozart
composer-of
plays
Performer
performs
composer-of
Smoke onthe water
typeMusic
Iron Maiden
type
Billy Cobham
typecomposer-of
plays
plays
The Phantomof the Opera
composed-by
composed-by
composer-of
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 27 / 39
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ILIADS The Algorithm
The AlgorithmStep by step
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01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters
05: for each couple (c, c
) of that type do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t11: return A
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ILIADS The Algorithm
The AlgorithmBuilding the alignment
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By now for each candidate pair of clusters of a given type A possible relationship has been explored A set of consequences has been inferred An inference-weighted similarity has been computed
Before restarting the loop: The axiom a(c,c) with the highest similarity score is chosen It is added to the output alignment A
If a(c,c) is an equivalence c and c are merged
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ILIADS The Algorithm
The AlgorithmBuilding the alignment
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By now for each candidate pair of clusters of a given type A possible relationship has been explored A set of consequences has been inferred An inference-weighted similarity has been computed
Before restarting the loop: The axiom a(c,c) with the highest similarity score is chosen It is added to the output alignment A
If a(c,c) is an equivalence c and c are merged
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 29 / 39
ILIADS The Algorithm
The AlgorithmStep by step
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01: Initialize algorithms structures (O is O1 O2)02: repeat:
03: Compute similarity scores between clusters
04: Heuristically select a type of clusters
05:for
each couple(c
,c
)of that type
do06: Determine a candidate relationship a(c,c)07: Perform incremental inference
08: Update similarity score
09: Select the best couple, update O and A
10: until there are clusters with similarity > t
11: return A
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 30 / 39
ILIADS The Algorithm
The AlgorithmThe End(ing)
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The algorithm halts when there are no more candidate clusters When there are no clusters having similarity greater than the
threshold t Remaining clusters are not likely to share any relationship
Intuitively ILIADS is guaranteed to terminate because Previously used cluster pairs are not re-used unless their score
changes The merging process decreases the number of clusters
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ILIADS The Algorithm
The AlgorithmThe End(ing)
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The algorithm halts when there are no more candidate clusters When there are no clusters having similarity greater than the
threshold t Remaining clusters are not likely to share any relationship
Intuitively ILIADS is guaranteed to terminate because Previously used cluster pairs are not re-used unless their score
changes The merging process decreases the number of clusters
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 31 / 39
ILIADS Experimental Results
Comparative resultsPrecision, Recall and F-1
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ILIADS Experimental Results
Tests Analysis
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The interplay between structure and instance integration delivershigher quality Significant improvement of recall Tests on ontologies without instance data showed comparable
results with the other systems
Lambda-tuning allows ILIADS to adapt itself better to particoular
pairs of ontologies
Inconsistent alignments due to limited inference steps were found
only in the .5% of tests
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ILIADS Experimental Results
Tests Analysis
-
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The interplay between structure and instance integration delivershigher quality Significant improvement of recall Tests on ontologies without instance data showed comparable
results with the other systems
Lambda-tuning allows ILIADS to adapt itself better to particoular
pairs of ontologies
Inconsistent alignments due to limited inference steps were found
only in the .5% of tests
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 33 / 39
ILIADS Conclusions and Future Developments
Summary
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Explicit semantics provided by ontologies can improve thequality of data integration
Instance data exploitation could significatly enhance traditional
matching techniques
ILIADS leverages both these opportunities and shows promising
results
Future developments
Inter-ontology differentiated parameters
Alignment of distant sections of the ontologies in parallel
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ILIADS Conclusions and Future Developments
Summary
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Explicit semantics provided by ontologies can improve thequality of data integration
Instance data exploitation could significatly enhance traditional
matching techniques
ILIADS leverages both these opportunities and shows promising
results
Future developments
Inter-ontology differentiated parameters
Alignment of distant sections of the ontologies in parallel
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 34 / 39
ILIADS Conclusions and Future Developments
Summary
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Explicit semantics provided by ontologies can improve thequality of data integration
Instance data exploitation could significatly enhance traditional
matching techniques
ILIADS leverages both these opportunities and shows promising
results
Future developments
Inter-ontology differentiated parameters
Alignment of distant sections of the ontologies in parallel
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 34 / 39 ILIADS Conclusions and Future Developments
Summary
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Explicit semantics provided by ontologies can improve thequality of data integration
Instance data exploitation could significatly enhance traditional
matching techniques
ILIADS leverages both these opportunities and shows promising
results
Future developments
Inter-ontology differentiated parameters
Alignment of distant sections of the ontologies in parallel
Savioli, Reale, Sorbini (DEIS) UGM07 SI-LS 2009 34 / 39 Demo
Demo
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And now an ultra-fancy demo
Savioli Reale Sorbini (DEIS) UGM07 SI-LS 2009 35 / 39 Appendix For Further Reading
For Further Reading I
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W3C OWL resources.
http://www.w3.org/2004/OWL/.
D. Aumueller, H.H. Do, S. Massmann, and E. Rahm.
Schema and ontology matching with COMA++.
In Proceedings of the 2005 ACM SIGMOD international
conference on Management of data, pages 906908. ACM NewYork, NY, USA, 2005.
I. Horrocks, P.F. Patel-Schneider, and F. van Harmelen.
From SHIQ and RDF to OWL: the making of a Web Ontology
Language.
Web Semantics: Science, Services and Agents on the World Wide
Web, 1(1):726, 2003.
Savioli Reale Sorbini (DEIS) UGM07 SI-LS 2009 36 / 39 Appendix For Further Reading
For Further Reading II
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Y. Kalfoglou and M. Schorlemmer.Ontology mapping: the state of the art.
The knowledge engineering review, 18(01):131, 2003.
E. Rahm and P.A. Bernstein.
A survey of approaches to automatic schema matching.
The VLDB Journal The International Journal on Very Large Data
Bases, 10(4):334350, 2001.
P. Shvaiko and J. Euzenat.
A survey of schema-based matching approaches.
Lecture Notes in Computer Science, 3730:146171, 2005.
Savioli Reale Sorbini (DEIS) UGM07 SI-LS 2009 37 / 39
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Appendix For Further Reading
-
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Thank you.
Group 15
A. Sorbini E. Savioli A. Reale
Savioli Reale Sorbini (DEIS) UGM07 SI-LS 2009 39 / 39
http://find/http://goback/
top related