A Survey of Approaches to Automatic Schema Matching

1
A Survey of Approaches to Automatic Schema
Matching
Erhard Rahm Philip A. Bernstein
The VLDB Journal 10334-350 (2001)
2
The Problem

• Schema matching
• Input schemas
• Output mappings
• Motivations
• Manual schema matching
• Generic and customizable schema matching

3
Application Domains

• Schema Integration Structures and Terminological
  relationships
• Data warehouses Source-to-warehouse
  Transformation
• E-commerce Message Translation
• Semantic query processing A Run-time Scenario

4
The Match Operator

• Representations of Input Schemas and Output
  Mapping
• Schema representation
• Schema elements
• Structure
• Mapping representation
• Mapping elements
• Mapping expressions
• Matching Function
• Mathematically unsatisfying
• Heuristics

5
Architecture for Generic Match
Tool 2 (E-business schemas)
Tool 1 (Portal schemas)
Tool 3 (Data warehousing schemas)
Global libraries (dictionaries, schemas, )
Schema import/export
Generic Match Implementation
Internal schema representation
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Classification of Approaches

• Individual matchers
• Instance vs Schema
• Element vs Structure Matching
• Language vs Constraint
• Matching Cardinality (11, 1n, n1, and nm)
• Auxiliary Information
• Combinations of multiple matchers

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Schema-level Approaches

• Granularity of match (element-level vs.
  structure-level)
• Match cardinality
• Linguistic approaches
• Constraint-based approaches
• Reusing schema and mapping information

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Granularity of match
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Match Cardinality
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Linguistic Approaches

• Name Matching
• Equality of names
• Equality of canonical name representations
• Equality of synonyms
• Equality of hypernyms
• Similarity of names based on common substrings,
  edit distance, pronunciation, and soundex
• User provided name matches
• Description Matching
• Ex. S1 empn //employee name
• Ex. S2 name //name of employee

11
Constraint-based Approaches
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Reusing Schema and Mapping Information
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Instance-level Approaches

• Linguistic characterization
• Information retrieval techniques
• Ex. Extracting keywords and themes
• Constraint-based characterization
• Numeric value ranges
• Numeric value averages
• Character patterns (PhoneNr, ISBNs,, SSNs)

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Combining Different Matchers

• Hybrid matchers
• Hard-wired combination of multiple matching
  criteria
• Better performance
• Composite matchers
• Independent basic matchers
• Flexible execution order

15
Sample Approaches

• SEMINT
• LSD
• SKAT
• TranScm
• DIKE
• ARTEMIS
• CUPID

16
Sample Approaches

• SEMINT
• LSD
• SKAT
• TranScm
• DIKE
• ARTEMIS
• CUPID

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Conclusion

• Propose a taxonomy that covers many of the
  existing approaches
• Suggest quantitative work on the relative
  performance and accuracy of different approaches