ER 2002BYU Data Extraction Group Automatically Extracting Ontologically Specified Data from HTML Tables with Unknown Structure David W. Embley, Cui Tao,

ER 2002BYU Data Extraction Group

Automatically Extracting Ontologically Specified Data

from HTML Tableswith Unknown Structure

David W. Embley, Cui Tao, Stephen W. Liddle

Brigham Young University

Funded by NSF


Information ExchangeSource Target

InformationExtraction

SchemaMatching

Leveragethis …

… to dothis


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 (336)835-85970002 1998 Elantra (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 stereo0002 a/c0003 Auto0003 jade green0003 gold


Schema Matching for HTML Tables with Unknown Structure


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: Factored Values


Problem: Split Values


Problem: Merged Values


Problem: Values not of Interest


Problem: Information Behind Links

Single-ColumnTable (formattedas 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: μ(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)* μ (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table

Legend

ACURA

ACURA


Solution: Replace Boolean Values

Legend

ACURA

ACURA

β CD Table

Yes,

CD

CD

Yes,Yes,βAutoβAir CondβAM/FMYes,

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>, <CD, >


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


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. πModelμ(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*TableπMakeμ(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*μ(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*TableπYearTable

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


πModelμ(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


πPriceTable



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,ρ Colour←Feature π ColourTable U ρ Auto←Feature π Auto β AutoTable U ρ Air Cond.←Feature π Air Cond.

β Air Cond.Table U ρ AM/FM←Feature π AM/FM β AM/FMTable U ρ CD←Featureπ CDβ CDTableYes, 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)


Conclusions• Summary

– Transformed schema-matching problem to extraction– Inferred semantic mappings– Discovered source-to-target mapping rules

• Evidence of Success– Tables (mappings): 95% (Recall); 100% (Precision)– Linked Text (value extraction): ~97% (Recall); ~86% (Precision)

• Future Work– Discover and exploit structure in linked text– Broaden table understanding– Integrate with current extraction tools

www.deg.byu.edu