A%20Classification%20of%20Schema-based%20Matching%20Approaches

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!