oMAP: An Implemented Framework for Automatically Aligning OWL Ontologies

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oMAP An Implemented Framework for Automatically
Aligning OWL Ontologies
Raphaël Troncy, Umberto Straccia ISTI-CNR straccia
_at_isti.cnr.it
SWAP, December, 2005
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Outline

• Motivations
• oMAP
• A formal framework
• The different classifiers used
• Evaluation
• Conclusion

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Motivations

• Heterogeneity of information systems
• Ontologies as a solution to data heterogeneity on
  the Web
• Ontologies are themselves heterogeneous
• knowledge representation language
• degree of formalization
• Semantic Web
• More and more OWL/RDF ontologies on the Web
• Need for comparing/reusing/merging ontologies
• partially covering the same domain
• different version of the same ontology

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Motivations (cont.)

• Distributed Information Retrieval
• Resource selection The agent has to select a
  subset of some relevant resources
• Query reformulation For every selected
  resource, the agent has to re-formulate its
  information need accordingly
• Data fusion rank aggregation The results from
  the selected resources have finally to be merged
  together.

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Aligning Ontologies

• A matching operator
• Input a set of discrete entities (tables, XML
  elements, classes, properties)
• Output
• relationship holding between the entities
  (subsumption, equivalence, disjointness)
• a confidence measure
• Automatic vs manual techniques
• Numerous work from various communities
• schema matching, machine learning, data
  integration

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Example
Equivalence Subsumption Disjointness
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oMAP A Formal Framework

• Inspirations
• Formal work in data exchange Fagin et al., 2003
• GLUE combining several specialized components
  for finding the best set of mappings Doan et
  al., 2003
• Notations
• A mapping is a tuple M (T, S, ?)
• S et T are the source and target ontologies
• Si is an OWL entity (class, datatype property,
  object property) of the ontology
• ? is a set of mapping rules aij Tj ? Si

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oMAP Overall Strategy

• A three step process
• Form possible ? sets and estimate its quality
  based on the quality measures for its mapping
  rules
• For each mapping rule Tj ? Si, estimate its
  confidence aij which also depends on the ? it
  belongs to
• Use heuristics to build iteratively the final set
  of mappings

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oMAP Combining Classifiers

• Weight of a mapping rule
• aij w (Si,Tj, ?)
• Using different classifiers
• w (Si,Tj,CLk) is the classifier's approximation
  of the rule Tj ? Si
• Combining the approximations
• Use of a priority list CL1 CL2 CLn

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Terminological Classifiers

• Same entity names (or URI)
• Same entity name stems

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Terminological Classifiers

• String distance name
• WordNet distance name
• lcs is the longest common substring between Si
  and Tj
• sim

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Machine Learning-Based Classifiers

• Collecting individuals
• label for the named individuals
• data value for the datatype properties
• type for the anonymous individuals and the range
  of object properties
• Recursion on the OWL definition
• depth parameter

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Machine Learning-Based Classifiers

• Example
• Individual (x1 type (Conference)
• value (label "Int. Conf. on WISE") value
  (location x2) )
• Individual (x2 type (Address)
• value (city "New York city") value (country
  "USA") )
• u1 ("Int. Conf. on WISE", "Address")
• u2 ("Address", "New York City", "USA")
• Naïve Bayes text classifier

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Structural and Semantics-Based Classifier

• If Si and Tj are property names
• If Si and Tj are concept names1

1 Where D D(Si) D(Tj) D(Si) represents the
set of concepts directly parent of Si
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Structural and Semantics-Based Classifier

• Let CS(QR.C) and DT(QR.D), then1
• Let CS(op C1Cm) and DT(op D1Dm), then2

1 Where Q,Q are quantifiers, R,R are property
names and C,D concept expressions 2 Where op, op
are concept constructors and n,m 1
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Structural and Semantics-Based Classifier

• Possible values for wop and wQ weights
• wop wQ

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Evaluation

• More and more techniques / tools for aligning
  ontologies
• difficult to compare all the approaches
  theoretically
• pragmatism evaluation campaign and contest
• I3CON based on the NIST Text Retrieval
  Conference model
• EON systematic benchmark tests on all OWL
  constructs
• OAEI http//oaei.inrialpes.fr
• Alignment API Euzenat, ISWC 2004
• common format for representing / exchanging the
  alignments found
• tools and metrics for evaluating these alignments

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• 3 series of tests on bibliographic ontologies
• simple tests identity, specialization/generalizat
  ion of the language
• systematic tests some features of the initial
  ontology are progressively discarded
• complex tests aligning 4 real ontologies
  available on the Web
• The directory real world case consists of
  aligning web sites directory using the large
  dataset

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Conclusion

• oMAP a formal framework for aligning
  automatically OWL ontologies
• Combining several specific classifiers
• terminological classifiers
• machine learning-based classifiers
• structural and semantics-based classifier
• Empirical evaluation on benchmark tests
• using traditional information retrieval metrics
• machine resources, memory, computation time not
  yet considered

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Future Work

• Alignment
• Using additional classifiers
• kNN, KL-distance, WordNet or other terminological
  resources
• straightforward theoretically but practically
  difficult
• Finding complex alignment
• name firstName lastName
• Distributed Information Retrieval
• Automated relevant resource selection

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Useful Links

• oMAP http//homepages.cwi.nl/troncy/oMAP/
• Tutorial Schema and Ontology Matching _at_ ESWC
  http//dit.unitn.it/accord/Presentations/ESWC'05-
  MatchingHandOuts.pdf
• Alignment API http//co4.inrialpes.fr/align/align
  .html
• OAEI http//oaei.inrialpes.fr/
• State of the Art
• P. Shvaiko and J. Euzenat A Survey of
  Shema-based Matching Approaches. Journal on Data
  Semantics (JoDS), 2005
• KW Consortium State of the Art on Ontology
  Alignment. Knowledge Web D2.2.3, 2004