The document presents the COLLONA project for collaborative ontology alignment. It discusses ontologies, ontology alignment, and the COLLONA system which allows humans and algorithms to collaborate on producing ontology mappings. Future work is aimed at improving synergies between humans and machines through supervised alignment, machine-user competition, and hybrid iterative refinement approaches. The goal is to develop COLLONA as an alignment service within the CLIC framework.

1. Collaborative Ontology Alignment
Final Project Presentation
Giorgos Papoutsakis
Jaakko Lapplaianen
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COLLONA

2. Outline
Ontology
Ontology Alignment
COLLaborative ONtology Alignment
Future Work
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3. Ontologies 101
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COLLONA

4. Ontologies - What
• What kinds of things exist or can exist in the world?
• what manner of relations can those things have to each other?
• Ontology is more concerned with what is possible
• Ontology in Computer Science
“An ontology formally represents knowledge as a set of concepts
within a domain, and the relationships between pairs of
concepts.”
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• Definition:
“An ontology is defined as a formal explicit specification of a
shared conceptualization”
• model a domain
• support reasoning about concepts.
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5. Ontologies - Web Ontology Language
Functional syntax
XML syntax
Manchester syntax
RDF/XML syntax
RDF/Turtle syntax
<Ontology ontologyIRI="http://example.com/tea.owl" ...>
Ontology(<http://example.com/tea.owl>
<rdf:RDF <Prefix name="owl"
...>
Declaration( Class( :Tea ) ) )
<http://example.com/tea.owl> rdf:type owl:Ontology .
Ontology: <http://example.com/tea.owl>
<owl:Ontology rdf:about=""/>
IRI="http://www.w3.org/2002/07/owl#"/>
:Tea rdf:typeTea
Class: owl:Class .
<owl:Class rdf:about="#Tea"/>
<Declaration> <Class IRI="Tea"/> </Declaration>
</rdf:RDF>
</Ontology>
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6. Ontologies - Example
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7. Ontology - Applications
Semantic web
Specify a data modeling representation
Specify standard conceptual vocabularies
Provide services for answering queries
Publish reusable knowledge bases
Offer services to facilitate interoperability across multiple
heterogeneous systems and databases
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8. Ontology alignment
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9. Alignment
• Classes
• Properties
• 3 kind of mappings
• Equivalence
• Subsumption
• Supersumption
• Examples
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• Sets of correspondences between elements of two ontologies
• Human = Person
• Lion < Animal
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10. Alignment API
• Framework for communication
• Well supported
• Representation Classes
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Ontology network
Alignment
Cell
Relation
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“The Alignment API is a set of abstractions for
expressing, accessing and sharing ontology alignments”
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11. Alignment applications
• Use alignments as links between ontologies
• Distributed information retrieval
• At query transformation step
• Understanding the semantics of the question
• At result fusion step
• Putting together the results
• Human – Machine dialogues
• Ontology evolution
• Instance migration
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• Avoid ontology merging
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12. Our work
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COLLONA

13. COLLONA
• Automatic methods and humans need to collaborate to
produce accurate results
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• Automatic alignment methods are not very accurate
• Humans can not handle heavy work load
• Humans are slow
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14. COLLONA – Core Tasks
• Visualization of Ontological pairs.
• Authoring of mappings by users.
• Visualization of automatically produced mapping.
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• Review of the main concepts regarding semi-automatic
alignment systems and standard APIs.
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15. COLLONA – Arch. & Data Flows
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16. COLLONA – Standard data formats
• From ontologies files
• From URI
• From alignment file
• Export
• Alignment file
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• Import
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17. COLLONA – First problems
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18. COLLONA – Further problems
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19. COLLONA - Divide & Conquer
• Panels for class information
• Properties
• Axioms
• Panels for mappings
• Class mappings
• Property Mappings
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• Graph shows only the class herierarchy
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20. COLLONA – More features
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21. COLLONA today
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22. Future work
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COLLONA

23. Future work - Synergies
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• The tool does not distinguish between machines and
users when importing/exporting mappings
• The user becomes a new alignment method
• This homogeneous feature allows
• Machine alignment / user validator
• Machine / user competition
• Hybrid approaches, complex data flows for iterative
mapping refinement
• Alignment as a Service – Synergy with CLIC project
• This is a step forward on ontology mapping quality
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24. User validation
Bad
computer!
Stupid
Computer!
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• Supervised alignment
• Algorithm evaluation and refinement
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25. Machine-user competition
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• Expert evaluation and training
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26. Hybrid approaches
Custome
Mappings
r
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• Combine human and machine workflows to iteratively refine
ontology mappings
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27. COLLONA @ CLIC Framework
Customer
Mappings
New Mappings
Mapping request
Customer
Negotiation layer
Negotiation
Alignment
service 1
Alignment service 2
Alignment service 3
Service registration, data
ALIGN!

28. Thank you!!
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COLLONA