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A Classification of Schema-based Matching Approaches

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Modal SAT. 13. MCN workshop, ISWC, 8th November 2004, Hiroshima, Japan. Heuristic Techniques ... Modal SAT (S-Match) The idea is to enhance propositional logics ... – PowerPoint PPT presentation

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Title: A Classification of Schema-based Matching Approaches


1
A Classification of Schema-based Matching
Approaches
Pavel Shvaiko
Meaning Coordination and Negotiation Workshop,
ISWC 8th November 2004, Hiroshima, Japan
2
Outline
  • Introduction
  • Classification of schema-based matching
    approaches
  • Matching systems
  • Conclusions
  • Future work

3
  • Introduction

4
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

5
Example Two XML schemas
HT
FT
6
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

7
Matching
8
  • Classification of Schema-based Matching
    Approaches

9
Schema matching approaches
Combined matchers
Taxonomy from E. Rahm, P. Bernstein, 2001
10
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

11
Schema Matching Approaches
12
Schema-based Matching Approaches
13
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
14
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
15
Matching Systems
16
Characteristics of state of the art matchers
Conclusions
17
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

18
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

19
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.

20
  • Thank you!
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