UFMG, June 2002BYU Data Extraction Group Automating Schema Matching for Data Integration David W. Embley Brigham Young University Funded by NSF

UFMG, June 2002BYU Data Extraction Group

Automating Schema Matchingfor Data Integration

David W. Embley

Brigham Young University

Funded by NSF


Information ExchangeSource Target

InformationExtraction

SchemaMatching

Leveragethis …

… to dothis


Presentation Outline

• Information Extraction

• Schema Matching for HTML Table

• Direct Schema Matching

• Indirect Schema Matching

• Conclusions and Future Work


Information Extraction


Extracting Pertinent Information from Documents


A Conceptual Modeling SolutionYear Price

Make Mileage

Model

Feature

PhoneNr

Extension

Car

hashas

has

has is for

has

has

has

1..*

0..1

1..*

1..* 1..*

1..*

1..*

1..*

0..1 0..10..1

0..1

0..1

0..1

0..*

1..*


Car-Ads OntologyCar [->object];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 “\d{2}”; context "([^\$\d]|^)[4-9]\d,[^\d]"; substitute "^" -> "19"; }, …End;


Recognition and Extraction

Car Year Make Model Mileage Price PhoneNr0001 1989 Subaru SW $1900 (363)835-85970002 1998 Elandra (336)526-54440003 1994 HONDA ACCORD EX 100K (336)526-1081

Car Feature0001 Auto0001 AC0002 Black0002 4 door0002 tinted windows0002 Auto0002 pb0002 ps0002 cruise0002 am/fm0002 cassette stero0002 a/c0003 Auto0003 jade green0003 gold


Schema Matching forHTML Tables


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


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}

?


Problem: Attribute is Value


Problem: Attribute-Value is Value

? ?


Problem: Value is not Value


Problem: Implied Values


Problem: Missing Attributes


Problem: Compound Attributes


Problem: Merged Values


Problem: Values not of Interest


Problem: Factored Values


Problem: Split Values


Problem: Information Behind Links

Single-ColumnTable (formateda list)

Tableextendingover severalpages


Solution

• Form attribute-value pairs (adjust if necessary)

• Do extraction

• Infer mappings from extraction patterns


Solution: Remove Internal Factoring

Discover Nesting: Make, (Model, (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*)*

Unnest: (Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table

Legend

ACURA

ACURA


Solution: Replace Boolean Values

Legend

ACURA

ACURA

CD Table

Yes,

CD

CD

Yes,Yes,AutoAir CondAM/FM

Yes,

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto


Solution: Form Attribute-Value Pairs

Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto

<Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto, Auto>, <Air Cond., Air Cond.>, <AM/FM, AM/FM>


Solution: Adjust Attribute-Value Pairs

Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto

<Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>


Solution: Do Extraction

Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto

<Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>


Solution: Infer Mappings

Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto

{Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}

Each row is a car. Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*TableMake(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*TableYearTable

Note: Mappings produce sets for attributes. Joining to form recordsis trivial because we have OIDs for table rows (e.g. for each Car).



Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto


Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table



Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto


PriceTable



Legend

ACURA

ACURA

CD

CD

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

AM/FM

Air Cond.

Air Cond.

Air Cond.

Air Cond.

Auto

Auto

Auto

Auto


Yes,ColourFeatureColourTable AutoFeatureAutoAutoTable Air Cond.FeatureAir Cond.

Air Cond.Table AM/FMFeatureAM/FMAM/FMTable CDFeatureCDCDTableYes, Yes, Yes,


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)


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)


Direct Schema Matching


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


Approach

• Target Schema T

• Source Schema S

• Framework– Individual Facet Matching– Combining Facets– Best-First Match Iteration


Example

Source Schema S

Car

Year

has

0:1

Make

has0:1

Modelhas

0:1

Cost

Style

has

has0:1

0:*

Year

has

0:1

Feature

has

0:*Cost

has0:1

Car

Mileage

has

Phone

has

0:10:1

Modelhas

0:1

Target Schema T

Make

has0:1

Miles

has0:1

Year

Model

Make YearMake

ModelCar

Car

Mileage Miles


Individual Facet Matching

• Attribute Names

• Data-Value Characteristics

• Expected Data Values


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


WordNet Rule

The number

of different common

hypernym roots of A

and B

The sum of distances of A and B to a

common hypernym

The sum of the

number of senses of A and B


Confidence Measures


Data-Value Characteristics

• C4.5 Decision Tree

• Features– Numeric data

(Mean, variation, standard deviation, …)

– Alphanumeric data(String length, numeric ratio, space ratio)


Confidence Measures


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


Confidence Measures


Combined Measures

Threshold: 0.5

10

000000

0 0 0 0 0 01

00000

0 0 0 0

10

0

0 0 0 000000

1

000

0 010 00

00

00


Final Confidence Measures

00

0


Experimental Results

• This schema, plus 6 other schemas– 32 matched attributes– 376 unmatched attributes

• Matched: 100%

• Unmatched: 99.5%– “Feature” ---”Color”– “Feature” ---”Body Type”

F193.8%

F284%

F392%

F198.9%

F297.9%

F398.4%

F1: WordNetF2: Value CharacteristicsF3: Expected Values


Indirect Schema Matching


Schema Matching

Source

Car

Year

Cost

Style

YearFeature

Cost

Phone

Target

Car

MilesMileage

Model

Make Make&

Model

Color

Body Type


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


Union and Selection

Car

Source

Year

Cost

Style

YearFeature

Cost

Phone

Target

Car

MilesMileage

Model

Make Make&

Model

Color

Body Type


Decomposition and Composition

Car

Source

Year

Cost

Style

YearFeature

Cost

Phone

Target

Car

MilesMileage

Model

Make Make&

Model

Color

Body Type


Structure

PO

POShipTo POBillTo POLines

City Street City Street Item

Count

Line Qty UoM

PurchaseOrder

DeliverToInvoiceTo

Items

ItemItemCount

ItemNumber

Quantity UnitOfMeasure

City Street

Address

Target Source

Example Taken From [MBR, VLDB’01]


Structure(Nonlexical Matches)

PO

POShipTo POBillTo POLines


Count

Line Qty UoM

PurchaseOrder

DeliverToInvoiceTo

Items

ItemCount

ItemNumber


City Street

Address

DeliverTo

Target Source


Structure(Join over FD Relationship Sets, …)

PO

POBillTo POLines


Count

Line Qty UoM

PurchaseOrder

InvoiceTo

Items

ItemCount

ItemNumber


City

Street City

Street

POShipTo DeliverTo

Target Source


Structure(Lexical Matches)

PO

POBillTo POLines


Count

Line Qty UoM

PurchaseOrder

InvoiceTo

Items

ItemCount

ItemNumber

Quantity

City

Street City

StreetCity

City

StreetStreet

City

City

Street

StreetCount

Count

Line QtyQuantity UnitOfMeasure

POShipTo DeliverTo

Target Source


Experiments• Methodology• Measures

– Precision

– Recall

– F Measure

falseposcorrect

correctprecision

falsenegcorrect

correctrecall

recallprecsion

F11

2


ResultsApplications

(Number of Schemes)

Precision

(%)

Recall

(%)

F

(%)

Correct False

Positive

False

Negative

Course Schedule (5) 98 93 96 119 2 9

Faculty Member (5) 100 100 100 140 0 0

Real Estate (5) 92 96 94 235 20 10

Data borrowed from Univ. of Washington [DDH, SIGMOD01]

Indirect Matches: 94% (precision, recall, F-measure)

Rough Comparison with U of W Results (Direct Matches only)

* Course Schedule – Accuracy: ~71%

* Faculty Members – Accuracy, ~92%

* Real Estate (2 tests) – Accuracy: ~75%


Conclusions and Future Work


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


Current & Future Work:Improve and Extend Indirect Matching

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


Current & Future Work:Tables Behind Forms

• Crawling the Hidden Web

• Filling in Forms from Global Queries


Current & Future Work:Developing Extraction Ontologies

• Creation from Knowledge Sources and Sample Application Pages K Ontology + Data Frames, Lexicons, …– RDF Ontologies

• User Creation by Example


Current & Future Work:and Much More …

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

www.deg.byu.edu