Automating Schema Matching

1
AutomatingSchema Matching

• David W. Embley, Cui Tao, Li Xu
• Brigham Young University

Funded by NSF
2
Information Exchange
Source
Target
Information Extraction
Schema Matching
3
Presentation Outline

• Information Extraction
• Schema Matching for Tables
• Direct Schema Matching
• Indirect Schema Matching
• Conclusions and Future Work

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Information Extraction
5
Extracting Pertinent Information from Documents
6
A Conceptual-Modeling Solution
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Car-Ads Ontology

• Car -gtobject
• Car 0..1 has Year 1..
• Car 0..1 has Make 1..
• Car 0...1 has Model 1..
• Car 0..1 has Mileage 1..
• Car 0.. has Feature 1..
• Car 0..1 has Price 1..
• PhoneNr 1.. is for Car 0..
• PhoneNr 0..1 has Extension 1..
• Year matches 4
• constant extract \d2
• context "(\\d)4-9\d
  \d"
• substitute "" -gt "19" ,
• End

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Recognition and Extraction
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Schema Matching for HTML Tables with Unknown
Structure
Cui Tao
10
Table-Schema Matching(Basic Idea)

• Many Tables on the Web
• Ontology-Based Extraction
• Works well for unstructured or semistructured
  data
• What about structured data tables?
• Method
• Form attribute-value pairs
• Do extraction
• Infer mappings from extraction patterns

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Problem Different Schemas

• Target Database Schema
• Car, Year, Make, Model, Mileage, Price,
  PhoneNr, PhoneNr, Extension, Car, Feature
• Different Source Table Schemas
• Run , Yr, Make, Model, Tran, Color, Dr
• Make, Model, Year, Colour, Price, Auto, Air
  Cond., AM/FM, CD
• Vehicle, Distance, Price, Mileage
• Year, Make, Model, Trim, Invoice/Retail, Engine,
  Fuel Economy

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Problem Attribute is Value
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Problem Attribute-Value is Value
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Problem Value is not Value
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Problem Implied Values
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Problem Missing Attributes
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Problem Compound Attributes
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Problem Factored Values
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Problem Split Values
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Problem Merged Values
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Problem Values not of Interest
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Problem Information Behind Links
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Solution

• Form attribute-value pairs (adjust if necessary)
• Do extraction
• Infer mappings from extraction patterns

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Solution Remove Internal Factoring
Discover Nesting Make, (Model, (Year, Colour,
Price, Auto, Air Cond, AM/FM, CD))
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Solution Replace Boolean Values
ACURA
ACURA
Legend
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Solution Form Attribute-Value Pairs
ACURA
ACURA
Legend
ltMake, Hondagt, ltModel, Civic EXgt, ltYear, 1995gt,
ltColour, Whitegt, ltPrice, 6300gt, ltAuto,
Autogt, ltAir Cond., Air Cond.gt, ltAM/FM, AM/FMgt,
ltCD, gt
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Solution Adjust Attribute-Value Pairs
ACURA
ACURA
Legend
ltMake, Hondagt, ltModel, Civic EXgt, ltYear, 1995gt,
ltColour, Whitegt, ltPrice, 6300gt, ltAutogt,
ltAir Condgt, ltAM/FMgt
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Solution Do Extraction
ACURA
ACURA
Legend
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Solution Infer Mappings
ACURA
ACURA
Legend
Car, Year, Make, Model, Mileage, Price,
PhoneNr, PhoneNr, Extension, Car, Feature
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Solution Do Extraction
ACURA
ACURA
Legend
Car, Year, Make, Model, Mileage, Price,
PhoneNr, PhoneNr, Extension, Car, Feature
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Solution Do Extraction
ACURA
ACURA
Legend
pPriceTable
Car, Year, Make, Model, Mileage, Price,
PhoneNr, PhoneNr, Extension, Car, Feature
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Solution Do Extraction
ACURA
ACURA
Legend
? Colour?Feature p ColourTable U ? Auto?Feature p
Auto ß AutoTable U ? Air Cond.?Feature p Air
Cond. ß Air Cond.Table U ? AM/FM?Feature p AM/FM
ß AM/FMTable U ? CD?Featurep CDß CDTable
Yes,
Yes,
Yes,
Yes,
Car, Year, Make, Model, Mileage, Price,
PhoneNr, PhoneNr, Extension, Car, Feature
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Experiment

• Tables from 60 sites
• 10 training tables
• 50 test tables
• 357 mappings (from all 60 sites)
• 172 direct mappings (same attribute and meaning)
• 185 indirect mappings (29 attribute synonyms, 5
  Yes/No columns, 68 unions over columns for
  Feature, 19 factored values, and 89 columns of
  merged values that needed to be split)

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Results

• 10 training tables
• 100 of the 57 mappings (no false mappings)
• 94.6 of the values in linked pages (5.4 false
  declarations)
• 50 test tables
• 94.7 of the 300 mappings (no false mappings)
• On the bases of sampling 3,000 values in linked
  pages, we obtained 97 recall and 86 precision
• 16 missed mappings
• 4 partial (not all unions included)
• 6 non-U.S. car-ads (unrecognized makes and
  models)
• 2 U.S. unrecognized makes and models
• 3 prices (missing or found MSRP instead)
• 1 mileage (mileages less than 1,000)

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Direct Schema Matching
Li Xu
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Attribute Matchingfor Populated Schemas

• Central Idea Exploit All Data Metadata
• Matching Possibilities (Facets)
• Attribute Names
• Data-Value Characteristics
• Expected Data Values
• Data-Dictionary Information
• Structural Properties

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Approach

• Target Schema T
• Source Schema S
• Framework
• Individual Facet Matching
• Combining Facets
• Best-First Match Iteration

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Example
Car
Car
Style
has
01
0
01
01
has
has
has
Cost
Mileage
Miles
Source Schema S
Target Schema T
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Individual Facet Matching

• Attribute Names
• Data-Value Characteristics
• Expected Data Values

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Attribute Names

• Target and Source Attributes
• T A
• S B
• WordNet
• C4.5 Decision Tree feature selection, trained on
  schemas in DB books
• f0 same word
• f1 synonym
• f2 sum of distances to a common hypernym root
• f3 number of different common hypernym roots
• f4 sum of the number of senses of A and B

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WordNet Rule
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Confidence Measures
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Data-Value Characteristics

• C4.5 Decision Tree
• Features
• Numeric data
• (Mean, variation, standard deviation, )
• Alphanumeric data
• (String length, numeric ratio, space ratio)

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Confidence Measures
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Expected Data Values

• Target Schema T and Source Schema S
• Regular expression recognizer for attribute A in
  T
• Data instances for attribute B in S
• Hit Ratio N'/N for (A, B) match
• N' number of B data instances recognized by the
  regular expressions of A
• N number of B data instances

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Confidence Measures
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Combined Measures
Threshold 0.5
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Final Confidence Measures
0
0
0
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Experimental Results

• This schema, plus 6 other schemas
• 32 matched attributes
• 376 unmatched attributes
• Measures
• Recall 100
• Precision 94
• F Measure 97
• False Positives
• Feature ---Color
• Feature ---Body Type

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Indirect Schema Matching
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Schema Matching
Color
Year
Year
Feature
Make
Make Model
Body Type
Cost
Model
Car
Style
Phone
Cost
Miles
Mileage
Source
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Mapping Generation

• Direct Matches as described earlier
• Attribute Names based on WordNet
• Value Characteristics based on value lengths,
  averages,
• Expected Values based on regular-expression
  recognizers
• Indirect Matches
• Direct matches
• Structure Evaluation
• Union
• Selection
• Decomposition
• Composition

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Union and Selection
Color
Year
Year
Feature
Make
Make Model
Body Type
Cost
Model
Car
Style
Phone
Cost
Miles
Mileage
Source
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Decomposition and Composition
Color
Year
Year
Feature
Make
Make Model
Body Type
Cost
Model
Car
Style
Phone
Cost
Miles
Mileage
Source
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Structure
Example Taken From MBR, VLDB01
PO
PurchaseOrder
Items
POShipTo
POBillTo
POLines
DeliverTo
InvoiceTo
Count
Address
Item
ItemCount
City
Street
City
Street
Item
ItemNumber
City
Street
Line
Qty
UoM
Quantity
UnitOfMeasure
Target
Source
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Structure(Nonlexical Matches)
PO
PurchaseOrder
Items
POShipTo
POBillTo
POLines
DeliverTo
InvoiceTo
DeliverTo
Count
Address
Item
Count
City
Street
City
Street
Item
ItemNumber
City
Street
Line
Qty
UoM
Quantity
UnitOfMeasure
Target
Source
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Structure(Join over FD Relationship Sets, )
PO
PurchaseOrder
Items
POBillTo
POLines
InvoiceTo
POShipTo
DeliverTo
City
Count
City
Item
Count
Street
City
Street
City
Street
Item
Street
ItemNumber
Line
Qty
UoM
Quantity
UnitOfMeasure
Target
Source
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Structure(Lexical Matches)
PO
PurchaseOrder
Items
POBillTo
POLines
InvoiceTo
POShipTo
DeliverTo
City
City
Count
Count
City
City
Item
Count
Street
Street
Count
City
Street
City
Street
Item
City
Street
City
Street
Street
Street
ItemNumber
Line
Qty
UoM
Line
Qty
Quantity
Quantity
UnitOfMeasure
Target
Source
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Experimental Results
Indirect Matches 94 (precision, recall,
F-measure)
Data borrowed from Univ. of Washington DDH,
SIGMOD01
Rough Comparison with U of W Results (Direct
Matches only) Course Schedule Accuracy
71 Faculty Members Accuracy, 92
Real Estate (2 tests) Accuracy 75
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Conclusions and Future Work
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Conclusions

• Table Mappings
• Tables 94.7 (Recall) 100 (Precision)
• Linked Text 97 (Recall) 86 (Precision)
• Direct Attribute Matching
• Matched 32 of 32 100 Recall
• 2 False Positives 94 Precision
• Direct and Indirect Attribute Matching
• Matched 494 of 513 96 Recall
• 22 False Positives 96 Precision

www.deg.byu.edu
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Current Future WorkImprove and Extend
Indirect Matching

• Improve Object-Set Matching (e.g. Lex/non-Lex)
• Add Relationship-Set Matching
• Computations

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Current Future WorkTables Behind Forms

• Crawling the Hidden Web
• Filling in Forms from Global Queries

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Current Future WorkDeveloping Extraction
Ontologies

• Creation from Knowledge Sources and Sample
  Application Pages
• µK Ontology Data Frames, Lexicons,
• RDF Ontologies
• User Creation by Example

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Current Future Workand Much More

• Table Understanding
• Microfilm Census Records
• Generate Ontologies by Reading Tables

www.deg.byu.edu