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Schema Matching and Data Extraction over HTML Tables - PowerPoint PPT Presentation

Schema Matching and Data Extraction over HTML Tables. Cui Tao Data Extraction Research Group Department of Computer Science Brigham Young University. supported by NSF. Introduction. Many tables on the Web How to integrate data stored in different tables? Detect the table of interest

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Schema Matching and Data Extraction over HTML Tables

Cui Tao

Data Extraction Research Group

Department of Computer Science

Brigham Young University

supported by NSF

• Many tables on the Web

• How to integrate data stored in different tables?

• Detect the table of interest

• Form attribute-value pairs (adjust if necessary)

• Do extraction

• Infer mappings from extraction patterns

ProblemDetecting The Table of Interest

Different schemas

• 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}

• Target database schema

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

{Car, Feature}

ProblemAttribute is Value

?

Problem Attribute-Value is Value

ProblemValue is not Value

ProblemFactored Values

ProblemSplit Values

ProblemMerged Values

extending

over several

pages

Single-Column

Table (formatted

as list)

• Detect the table of interest

• Form attribute-value pairs (adjust if necessary)

• Do extraction

• Infer mappings from extraction patterns

SolutionDetect The Table of Interest

• ‘Real’ table test

• Same number of values

• Table size

• Attribute test

• Density measure test

# of ontology extracted values

total # of values in the table

2001

2001

2000

2000

2000

2000

2000

2000

1999

1999

Solution Remove Factoring

SolutionReplace Boolean Values

SolutionForm Attribute-Value Pairs

<Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, \$6300>,

<Auto, Auto>, <Air Cond., Air Cond.>, <AM/FM, AM/FM>

<Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, \$6300>,

<Auto, Auto>, <Air Cond., Air Cond.>, <AM/FM, AM/FM>

semi-structured:

concatenate

<

Single attribute value pairs:

Pair them together

<Price, \$7,988>, <Mileage, 63,168 miles>, <Body Type, Car>, <Body Style, 4 DR Sedan>, <Transmission, Automatic>, <Engine, 3.0 L V-6>, <Doors, 4>, <Fuel Type, Gas>, <Stock Number, 22764>, <VIN, 1FAFP52U2WA139879>

List:

Mark the beginning

and the end

>

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

SolutionInferred Mapping Creation

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

• 10 “training” tables

• 100% of the 57 mappings (no false mappings)

• 94.6% precision 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

• Cell Phone Plan Application domain

• Soccer Player Application domain

• Provides an approach to extract information automatically from HTML tables

• Suggests a different way to solve the problem of schema matching