A Classification of Schema-based Matching Approaches

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A Classification of Schema-based Matching
Approaches
Pavel Shvaiko
Meaning Coordination and Negotiation Workshop,
ISWC 8th November 2004, Hiroshima, Japan
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Outline

• Introduction
• Classification of schema-based matching
  approaches
• Matching systems
• Conclusions
• Future work

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• Introduction

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Semantic Web and the Match operator

• Information sources (e.g., database schemas,
  taxonomies or ontologies) can be viewed as
  graph-like structures containing terms and their
  inter-relationships
• Match is one of the key operators for enabling
  the Semantic Web since it takes two graph-like
  structures and produces a mapping between the
  nodes of the graphs that correspond
  semantically to each other

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Example Two XML schemas
HT
FT
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Schema matching vs Ontology alignment

• Differences
• Database schemas often do not provide explicit
  semantics for their data
• Ontologies are logical systems that themselves
  incorporate semantics (intuitive or formal)
• E.g., ontology definitions as a set of logical
  axioms
• Ontology data models are richer (the number of
  primitives is higher, and they are more complex)
  then schema data models
• E.g., OWL allows defining new classes as unions
  or intersections of other classes
• Commonalities
• Ontologies can be viewed as schemas for knowledge
  bases
• Techniques developed for both problems are of a
  mutual benefit

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Matching
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• Classification of Schema-based Matching
  Approaches

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Schema matching approaches
Combined matchers
Taxonomy from E. Rahm, P. Bernstein, 2001
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Semantic view on matching
What is missing in the taxonomy of schema
matching approaches we have just seen ?
Two new criteria

• Heuristic vs formal
• heuristic techniques try to guess relations which
  may hold between similar labels or graph
  structures
• formal techniques have model-theoretic semantics
  which is used to justify their results
• Implicit vs explicit
• Implicit techniques are syntax driven techniques
• E.g., techniques, which consider labels as
  strings, or analyze data types, or soundex of
  schema/ontology elements
• Explicit techniques exploit the semantics of
  labels
• E.g., thesauruses, ontologies

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Schema Matching Approaches
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Schema-based Matching Approaches
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Heuristic Techniques

• Element-level explicit techniques
• Precompiled dictionary (Cupid, COMA)
• E.g., syn key - "NKNNikon syn
• Lexicons (S-Match, CTXmatch)
• E.g., WordNet Camera is a hypernym for
  Digital Camera,
• therefore, Digital_Cameras ? Photo_and_Cameras
• Structure-level explicit techniques
• Taxonomic structure (Anchor-Prompt, NOM)
• E.g., Given that Digital_Cameras ?
  Photo_and_Cameras, then
• FJFLM and FujiFilm can be found as an
  appropriate match

Example
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Formal Techniques

• Structure-level explicit techniques
• Propositional satisfiability (SAT) (S-Match,
  CTXmatch)
• The approach is to translate the matching
  problem, namely the two graphs (trees) and
  mapping queries into propositional formula and
  then to check it for its validity
• Modal SAT (S-Match)
• The idea is to enhance propositional logics
  with modal logic (or ALC DL) operators.
  Therefore, the matching problem is translated
  into a modal logic formula which is further
  checked for its validity using sound and
  complete satisfiability search procedures.

Example
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Matching Systems
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Characteristics of state of the art matchers
Conclusions
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Uses of Classification

• The classification proposed provides a common
  conceptual basis, and hence can be used for
  comparing (analytically) different existing
  schema/ontology matching systems
• It can help in designing a new matching system,
  or an elementary matcher, taking advantages of
  state of the art solutions

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Future Work

• Provide a more detailed view on the general
  properties of matching algorithms
• Add to the classification language-based
  techniques, e.g., tokenization, lemmatization,
  elimination
• Extend classification by taking into account
  DL-based matchmaking solutions
• Extend classification by adding new appearing
  matching techniques and systems implementing
  them, e.g., OLA, QOM
• Compare matching systems also experimentally,
  with the help of benchmarks

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References

• Knowledge Web project http//knowledgeweb.semanti
  cweb.org/
• Project website at DIT - ACCORD
  http//www.dit.unitn.it/accord/
• P. Shvaiko A classification of schema-based
  matching approaches. Technical Report, DIT-04-93,
  University of Trento, 2004.
• E. Rahm, P. Bernstein A survey of approaches to
  automatic schema matching. In Very Large
  Databases Journal, 10(4)334-350, 2001.
• F. Giunchiglia, P.Shvaiko Semantic matching. In
  The Knowledge Engineering Review Journal,
  18(3)265-280, 2003.
• P. Bouquet, L. Serafini, S. Zanobini Semantic
  coordination a new approach and an application.
  In Proceedings of ISWC, 130-145, 2003.

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• Thank you!