ontology alignment representation

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Ontology Alignment Representation Fran¸coisScharffe Semantic Technology Institute (STI) University of Innsbruck, Austria April 14, 2008 Fran¸ cois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 1 / 34

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An ontology alignment representation framework

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Page 1: Ontology alignment representation

Ontology Alignment Representation

Francois Scharffe

Semantic Technology Institute (STI)University of Innsbruck, Austria

April 14, 2008

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 1 / 34

Page 2: Ontology alignment representation

Outline

IntroductionOntology mediationExample

Actual limitations of the approach

Three levels of knowledge abstractionSPARQL for instance transformationThe Alignment Format and Ontology

Correspondence PatternsPatternsPattern templatePattern libraryDatabase mapping to ontologies patterns

Conclusion

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 2 / 34

Page 3: Ontology alignment representation

Introduction Ontology mediation

Situation

I Many ontologies overlap

I Need to exchange data between applications/services/agents

I Ontology mediation is the set of techniques making this dataexchange possible

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 3 / 34

Page 4: Ontology alignment representation

Introduction Ontology mediation

Situation

I Many ontologies overlap

I Need to exchange data between applications/services/agents

I Ontology mediation is the set of techniques making this dataexchange possible

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 3 / 34

Page 5: Ontology alignment representation

Introduction Ontology mediation

Situation

I Many ontologies overlap

I Need to exchange data between applications/services/agents

I Ontology mediation is the set of techniques making this dataexchange possible

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 3 / 34

Page 6: Ontology alignment representation

Introduction Ontology mediation

Two phases ontology mediation

Two phases can be distiguished in ontology mediation:

I Constructing an alignment (matching, graphical tool).

I Processing the alignment for a mediation task (data translation,ontology merging, query rewriting)

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 4 / 34

Page 7: Ontology alignment representation

Introduction Ontology mediation

Two phases ontology mediation

Two phases can be distiguished in ontology mediation:

I Constructing an alignment (matching, graphical tool).

I Processing the alignment for a mediation task (data translation,ontology merging, query rewriting)

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 4 / 34

Page 8: Ontology alignment representation

Introduction Ontology mediation

Ontology Alignment

Definition (Alignment, correspondence)

Given two ontologies o and o ′, an alignment between o and o ′ is a set ofcorrespondences (i.e., 4-uples): 〈e, e ′, r , n〉 with

I e ∈ o and e ′ ∈ o ′ being the two matched entities,

I r being a relationship holding between e and e ′, and

I n expressing the level of confidence [0..1] in this correspondence.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 5 / 34

Page 9: Ontology alignment representation

Introduction Example

Example 1: wines from Bordeaux

A BordeauxWine is a wine produced in the Bordeaux region.

ClassCorrespondence

wine:BordeauxWine vin:Vin

vin:terroir

geo:LocationAttribute value restriction

Attribute value restriction pattern

http://.../Château-Margaux

http://.../Château-Marguaux

vin:terroir

geo:Bordelais

Figure: Bordeaux wines

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 6 / 34

Page 10: Ontology alignment representation

Actual limitations of the approach

Problem

I while many matching algorithms, many graphical tools are out there,there is not something such as a common format that would allow toexchange alignments they output.

I while the correspondence such as the one in the precedent exampleoccurs frequently, no algorithm is able to automatically detect it.

I We cope with these issues in the following way

I We provide an alignment representation formalism allowingexchange of alignments through their lifecycle

I We formalize aspects of recurring correspondences and extractpatterns that will provide support for constructing alignments.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 7 / 34

Page 11: Ontology alignment representation

Actual limitations of the approach

Problem

I while many matching algorithms, many graphical tools are out there,there is not something such as a common format that would allow toexchange alignments they output.

I while the correspondence such as the one in the precedent exampleoccurs frequently, no algorithm is able to automatically detect it.

I We cope with these issues in the following way

I We provide an alignment representation formalism allowingexchange of alignments through their lifecycle

I We formalize aspects of recurring correspondences and extractpatterns that will provide support for constructing alignments.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 7 / 34

Page 12: Ontology alignment representation

Actual limitations of the approach

Problem

I while many matching algorithms, many graphical tools are out there,there is not something such as a common format that would allow toexchange alignments they output.

I while the correspondence such as the one in the precedent exampleoccurs frequently, no algorithm is able to automatically detect it.

I We cope with these issues in the following way

I We provide an alignment representation formalism allowingexchange of alignments through their lifecycle

I We formalize aspects of recurring correspondences and extractpatterns that will provide support for constructing alignments.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 7 / 34

Page 13: Ontology alignment representation

Actual limitations of the approach

Problem

I while many matching algorithms, many graphical tools are out there,there is not something such as a common format that would allow toexchange alignments they output.

I while the correspondence such as the one in the precedent exampleoccurs frequently, no algorithm is able to automatically detect it.

I We cope with these issues in the following way

I We provide an alignment representation formalism allowingexchange of alignments through their lifecycle

I We formalize aspects of recurring correspondences and extractpatterns that will provide support for constructing alignments.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 7 / 34

Page 14: Ontology alignment representation

Actual limitations of the approach

Problem

I while many matching algorithms, many graphical tools are out there,there is not something such as a common format that would allow toexchange alignments they output.

I while the correspondence such as the one in the precedent exampleoccurs frequently, no algorithm is able to automatically detect it.

I We cope with these issues in the following way

I We provide an alignment representation formalism allowingexchange of alignments through their lifecycle

I We formalize aspects of recurring correspondences and extractpatterns that will provide support for constructing alignments.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 7 / 34

Page 15: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 16: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 17: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 18: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 19: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 20: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 21: Ontology alignment representation

Three levels of knowledge abstraction

I three levels of abstraction for ontology alignment representation

I at the ground level: mediation rules or grounded correspondences(expressed in OWL, COWL, SWRL, WSML variants, SPARQL++,etc.).

I at the intermediate level: alignment (expressed in a particular rule orobject model of a tool/algorithm)

I at the top level of abstraction: correspondence patterns (newconcept)

We implement this framework on the three levels.

I at the ground level: a SPARQL extension for data transformation

I at the intermediate level: the Alignment format and the AlignmentOntology

I at the top level: a library of recurring correspondence patterns

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 8 / 34

Page 22: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

SPARQL++ for instance transformation

We propose to use SPARQL as a convenient data translation language forRDF.

I it allows to query data

I its CONSTRUCT statement allows to translate graphs fragments

I It’s a W3C recommendation and is already supported by numeroustools

I However, features of SPARQL are not mature enough to expresscomplex mappings: we thus propose an extension:SPARQL++

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 9 / 34

Page 23: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

SPARQL++ for instance transformation

We propose to use SPARQL as a convenient data translation language forRDF.

I it allows to query data

I its CONSTRUCT statement allows to translate graphs fragments

I It’s a W3C recommendation and is already supported by numeroustools

I However, features of SPARQL are not mature enough to expresscomplex mappings: we thus propose an extension:SPARQL++

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 9 / 34

Page 24: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

SPARQL++ for instance transformation

We propose to use SPARQL as a convenient data translation language forRDF.

I it allows to query data

I its CONSTRUCT statement allows to translate graphs fragments

I It’s a W3C recommendation and is already supported by numeroustools

I However, features of SPARQL are not mature enough to expresscomplex mappings: we thus propose an extension:SPARQL++

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 9 / 34

Page 25: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

SPARQL++ for instance transformation

We propose to use SPARQL as a convenient data translation language forRDF.

I it allows to query data

I its CONSTRUCT statement allows to translate graphs fragments

I It’s a W3C recommendation and is already supported by numeroustools

I However, features of SPARQL are not mature enough to expresscomplex mappings: we thus propose an extension:SPARQL++

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 9 / 34

Page 26: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Simple example (Translate from VCard to FOAF)

vCard

VCard:FN

FOAF

foaf:name

CONSTRUCT { ?X foaf:name ?Y }WHERE { ?X VCard:FN ?Y }

Easy! Supported by standard SPARQL

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 10 / 34

Page 27: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Simple example (Translate from VCard to FOAF)

vCard

VCard:FN

FOAF

foaf:name

CONSTRUCT { ?X foaf:name ?Y }WHERE { ?X VCard:FN ?Y }

Easy! Supported by standard SPARQL

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 10 / 34

Page 28: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Built-in functions and value generation

vCard FOAF

foaf:name

VCard:Family

VCard:Given

CONSTRUCT { ?X foaf:name fn:concat(?N," ",?F }WHERE { ?X VCard:Given ?N. ?X VCard:Family ?F }

This is not possible in standard SPARQL

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 11 / 34

Page 29: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Built-in functions and value generation

vCard FOAF

foaf:name

VCard:Family

VCard:Given

CONSTRUCT { ?X foaf:name fn:concat(?N," ",?F }WHERE { ?X VCard:Given ?N. ?X VCard:Family ?F }

This is not possible in standard SPARQL

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 11 / 34

Page 30: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: Aggregates

Translate from DOAP to RDF Open Source Software Vocabulary

CONSTRUCT { ?P os:latestReleaseMAX(?V : ?P doap:release ?R. ?R doap:revision ?V) }

WHERE { ?P rdf:type doap:Project . }

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 12 / 34

Page 31: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: Aggregates

Translate from DOAP to RDF Open Source Software Vocabulary

CONSTRUCT { ?P os:latestReleaseMAX(?V : ?P doap:release ?R. ?R doap:revision ?V) }

WHERE { ?P rdf:type doap:Project . }

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 12 / 34

Page 32: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: regular path expressions (pSPARQL)

o1

o1:parentOf

o2

o2:ancestorOf

⊆+

CONSTRUCTs with regular path expressions can cater for that, this ispossible in pSPARQL:

CONSTRUCT { ?X o2:ancestor ?Y }WHERE { ?X o1:parentOf+ ?Y . }

pSPARQL offers even more: full regular expressions over paths conditionsover paths, etc.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 13 / 34

Page 33: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: regular path expressions (pSPARQL)

o1

o1:parentOf

o2

o2:ancestorOf

⊆+

CONSTRUCTs with regular path expressions can cater for that, this ispossible in pSPARQL:

CONSTRUCT { ?X o2:ancestor ?Y }WHERE { ?X o1:parentOf+ ?Y . }

pSPARQL offers even more: full regular expressions over paths conditionsover paths, etc.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 13 / 34

Page 34: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: regular path expressions (pSPARQL)

o1

o1:parentOf

o2

o2:ancestorOf

⊆+

CONSTRUCTs with regular path expressions can cater for that, this ispossible in pSPARQL:

CONSTRUCT { ?X o2:ancestor ?Y }WHERE { ?X o1:parentOf+ ?Y . }

pSPARQL offers even more: full regular expressions over paths conditionsover paths, etc.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 13 / 34

Page 35: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

Example: regular path expressions (pSPARQL)

o1

o1:parentOf

o2

o2:ancestorOf

⊆+

CONSTRUCTs with regular path expressions can cater for that, this ispossible in pSPARQL:

CONSTRUCT { ?X o2:ancestor ?Y }WHERE { ?X o1:parentOf+ ?Y . }

pSPARQL offers even more: full regular expressions over paths conditionsover paths, etc.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 13 / 34

Page 36: Ontology alignment representation

Three levels of knowledge abstraction SPARQL for instance transformation

I SPARQL++ was published at OTM 2007. The paper contains detailsof the extension, semantics, and implementation details.

I pSPARQL integration in our alignment framework was published atOnAv 2008.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 14 / 34

Page 37: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Motivation and requirements

I considering the alignment as a first-class citizen

I being independent from the ontology formalism

I being able to represent correspondences between ontological entities

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 15 / 34

Page 38: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Motivation and requirements

I considering the alignment as a first-class citizen

I being independent from the ontology formalism

I being able to represent correspondences between ontological entities

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 15 / 34

Page 39: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Motivation and requirements

I considering the alignment as a first-class citizen

I being independent from the ontology formalism

I being able to represent correspondences between ontological entities

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 15 / 34

Page 40: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 41: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 42: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 43: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 44: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 45: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 46: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 47: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Ontology

I Models the ontology alignment domain

I Classes, Attributes, Relations and Instances for the entities

I One to one, many to many, using set operators on the entities

I Heterogeneous correspondences

I Conditions can restrict entities scope

I Transformations of attributes values

I Paths

I Model theoretic based semantics

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 16 / 34

Page 48: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The Alignment Format

Example serialization of Cell instances (RDF/XML):FOAF persons based in Innsbruck have a particular VCard phone extension:

<Cell><entity1><Class rdf:about="&foaf;Person"><attributeValueCondition><Restriction><onProperty rdf:resource="&foaf;based_near"/><comparator rdf:datatype="&xsd;string">xsd:equals</comparator><value rdf:datatype="&xsd;string">Fortaleza</value>

</Restriction></attributeValueCondition></Class>

</entity1>

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 17 / 34

Page 49: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

The alignment format as an exchange format (2)

<entity2><Class rdf:about="&v;VCard"><attributeValueCondition><Restriction><onProperty rdf:resource="&v;workTel"/><comparator rdf:datatype="&xsd;string">xsd:startsWith</comparator><value rdf:datatype="&xsd;string">+0043512</value></Restriction>

</attributeValueCondition></Class>

</entity2><measure RDF:datatype=’&BSD;float’>1.0</measure><relation>equivalence</relation></Cell>

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 18 / 34

Page 50: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Alignments semantics

Based on [Zimmermann 2006] and largely compliant with descriptionlogics.

Syntax level o o ′

Local semantics level D D ′I I ′

Global semantics level Dε ε′

A

I

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 19 / 34

Page 51: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Alignments semantics

Based on [Zimmermann 2006] and largely compliant with descriptionlogics.

Syntax level o o ′

Local semantics level D D ′I I ′

Global semantics level Dε ε′

A

I

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 19 / 34

Page 52: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Alignments semantics

Based on [Zimmermann 2006] and largely compliant with descriptionlogics.

Syntax level o o ′

Local semantics level D D ′I I ′

Global semantics level Dε ε′

A

I

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 19 / 34

Page 53: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Alignments semantics

Based on [Zimmermann 2006] and largely compliant with descriptionlogics.

Syntax level o o ′

Local semantics level D D ′I I ′

Global semantics level Dε ε′

A

I

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 19 / 34

Page 54: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Entity semantics

The semantics of expressions are interpreted within D through:

∀x ∈ QL(o), ε o I (x) ∈ D

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 20 / 34

Page 55: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Expression semantics

The expression semantics is typically the semantics of a description logic:

I (c) = ε o I (c)

I (C t C ′) = I (C ) ∪ I (C ′)

I (C u C ′) = I (C ) ∩ I (C ′)

I (¬C ) = D − I (C )

I (∃K ) = {x ∈ D|∃y ∈ D; 〈x , y〉 ∈ I (K )}

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 21 / 34

Page 56: Ontology alignment representation

Three levels of knowledge abstraction The Alignment Format and Ontology

Alignment processing

The combined alignment API (INRIA) and Mapping API allow to parsealignments and ground them for execution.

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 22 / 34

Page 57: Ontology alignment representation

Correspondence Patterns Patterns

Patterns in the literature

I Capture recurring knowledge

I Facilitate the design task by using known structures

I Facilitate semi-automatic design

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 23 / 34

Page 58: Ontology alignment representation

Correspondence Patterns Patterns

Patterns in the literature

I Capture recurring knowledge

I Facilitate the design task by using known structures

I Facilitate semi-automatic design

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 23 / 34

Page 59: Ontology alignment representation

Correspondence Patterns Patterns

Patterns in the literature

I Capture recurring knowledge

I Facilitate the design task by using known structures

I Facilitate semi-automatic design

Francois Scharffe (STI Innsbruck) Ontology Alignment Representation April 14, 2008 23 / 34

Page 60: Ontology alignment representation

Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Page 64: Ontology alignment representation

Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Page 65: Ontology alignment representation

Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Page 66: Ontology alignment representation

Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Page 67: Ontology alignment representation

Correspondence Patterns Pattern template

Pattern template elements

I Name: give a meaningful name for the pattern, corresponds to afragment of the URI associated with the pattern.

I Alias: alternative name

I Problem: A statement describing the intent, goals of the patterns,which entities are involved in the correspondence.

I Context: refers to the context of usage for the pattern (specificdomain, specific application)

I Solution: describe the result of using the pattern.

I Example: example alignment representing this pattern

I Related Patterns: relationships between this pattern and others

I Known uses: URIs of existing alignments using this patterns

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Correspondence Patterns Pattern template

Example pattern: Aggregation pattern

os:Software

doap:realease

os:LatestVersion

doap:Version

Hoary Hedgehog

Warty Warthog

...Hardy Heron

doap:realease

"4.10"

"8.04"...

"5.04"

doap:Project

Attribute Value aggregation (MAX)

"8.04"

Attribute Correspondence

ClassCorrespondence

valuesdoap:revision

Aggregation Pattern

Stringhttp://www.ubuntu.org/

os:LatestVersion

doap:revision

doap:revision

doap:revision...

http://www.ubuntu.org/

Figure: Aggregation Pattern

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Correspondence Patterns Pattern template

Example pattern: RCA-A pattern

RCA-A concatPattern

vc:VCard

vc:family-name

vc:Nfoaf:name

foaf:Personvc:name

value

Attribute RelationCorrespondence

Class AttributeCorrespondence

Value TransformationConcatenation

vc:given-name

vc:additional-name

http://.../francois

vc:name

_:bn01

"Scharffe"

"François"

"Vincent Alfred"

http://.../francois

foaf:name

"François Vincent Alfred Scharffe"

Figure: Relation Class Attribute to Attribute Pattern

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Correspondence Patterns Pattern library

Pattern library

I Implemented as an ontology extending the Alignment Ontology withspecific patterns properties.

I Sets of placeholders entities to be replaced during patternsinstantiation.

I Around 40 patterns available.

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Correspondence Patterns Pattern library

Pattern library

I Implemented as an ontology extending the Alignment Ontology withspecific patterns properties.

I Sets of placeholders entities to be replaced during patternsinstantiation.

I Around 40 patterns available.

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Correspondence Patterns Pattern library

Pattern library

I Implemented as an ontology extending the Alignment Ontology withspecific patterns properties.

I Sets of placeholders entities to be replaced during patternsinstantiation.

I Around 40 patterns available.

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Correspondence Patterns Pattern library

Pattern library(2)

Figure: Overview of the patterns library

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Correspondence Patterns Database mapping to ontologies patterns

Database mapping to ontologies

Direct Mapping A table in the database directly corresponds to a conceptin the ontology. There is a one-to-one correspondencebetween records in the table and instances of the concept.This pattern is modeled on the basis of the Class equivalencecorrespondence pattern.

Join/Union A set of database tables corresponds to a concept in theontology when they are joined. There is a one-to-onecorrespondence between join records of the joined tables andinstances of an ontology concept. This pattern is modeledon the Class Union pattern.

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Correspondence Patterns Database mapping to ontologies patterns

Database mapping to ontologies (2)

Projection A subset of the columns of a database table are needed tomap a concept in the ontology. This pattern is modeled onthe basis of the Class By Attribute Type pattern, where thescope of the class (the table) is restricted to the specificattributes (columns).

Selection A subset of the rows of a database table map a concept inthe ontology. This pattern is modeled on the basis of theClass By Attribute Value, rrestrictingthe scope of the class(the table) to only those instances (the table rows) having agiven value for the specified aattributes(columns).

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Correspondence Patterns Database mapping to ontologies patterns

Database mapping to ontologies (3)

Value transformation A column in an table corresponds to an attribute inthe ontology after transformation of its value using atransformation function. This pattern is modeled on thebasis of the Attribute Transformation pattern.

Combinations Combinations of the aforementioned patterns are alsopossible.

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Conclusion

Conclusion

This thesisI Introduces a new three layered framework reflecting the ontology

mediation landscape:

1. Mediation rules with the introduction of a SPARQL extension forinstance transformation.

2. Ontology Alignment with the introduction of a alignmentrepresentation formalism: language and ontology.

3. Correspondence patterns introduced, formalized, reference patternslibrary.

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Conclusion

Conclusion

This thesisI Introduces a new three layered framework reflecting the ontology

mediation landscape:

1. Mediation rules with the introduction of a SPARQL extension forinstance transformation.

2. Ontology Alignment with the introduction of a alignmentrepresentation formalism: language and ontology.

3. Correspondence patterns introduced, formalized, reference patternslibrary.

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Page 79: Ontology alignment representation

Conclusion

Conclusion

This thesisI Introduces a new three layered framework reflecting the ontology

mediation landscape:

1. Mediation rules with the introduction of a SPARQL extension forinstance transformation.

2. Ontology Alignment with the introduction of a alignmentrepresentation formalism: language and ontology.

3. Correspondence patterns introduced, formalized, reference patternslibrary.

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Page 80: Ontology alignment representation

Conclusion

Conclusion

This thesisI Introduces a new three layered framework reflecting the ontology

mediation landscape:

1. Mediation rules with the introduction of a SPARQL extension forinstance transformation.

2. Ontology Alignment with the introduction of a alignmentrepresentation formalism: language and ontology.

3. Correspondence patterns introduced, formalized, reference patternslibrary.

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Conclusion

Ongoing and Future works

I Write the thesis !

I Groundings to SPARQL++

I Extend the pattern library with domain and application specificpatterns.

I Study how patterns can assist matching algorithms to discovercomplex matches.

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Page 82: Ontology alignment representation

Conclusion

Ongoing and Future works

I Write the thesis !

I Groundings to SPARQL++

I Extend the pattern library with domain and application specificpatterns.

I Study how patterns can assist matching algorithms to discovercomplex matches.

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Page 83: Ontology alignment representation

Conclusion

Ongoing and Future works

I Write the thesis !

I Groundings to SPARQL++

I Extend the pattern library with domain and application specificpatterns.

I Study how patterns can assist matching algorithms to discovercomplex matches.

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Page 84: Ontology alignment representation

Conclusion

Ongoing and Future works

I Write the thesis !

I Groundings to SPARQL++

I Extend the pattern library with domain and application specificpatterns.

I Study how patterns can assist matching algorithms to discovercomplex matches.

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Conclusion

Thank you for your attention !

Questions ?

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