Toward Tomorrow’s Semantic Web An Approach Based on Information Extraction Ontologies David W....

Toward Tomorrow’s Semantic WebAn Approach Based on

Information Extraction Ontologies

David W. EmbleyBrigham Young University

Funded in part by the National Science Foundation

Presentation Outline Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges

Grand Challenge

Semantic UnderstandingSemantic Understanding

Can we quantify & specify the nature of this grand challenge?

Grand Challenge

Semantic UnderstandingSemantic Understanding“If ever there were a technology that could generatetrillions of dollars in savings worldwide …, it wouldbe the technology that makes business informationsystems interoperable.”

(Jeffrey T. Pollock, VP of Technology Strategy, Modulant Solutions)

Grand Challenge

Semantic UnderstandingSemantic Understanding“The Semantic Web: … content that is meaningful tocomputers [and that] will unleash a revolution of newpossibilities … Properly designed, the Semantic Webcan assist the evolution of human knowledge …”

(Tim Berners-Lee, …, Weaving the Web)

Grand Challenge

Semantic UnderstandingSemantic Understanding“20th Century: Data Processing“21st Century: Data Exchange “The issue now is mutual understanding.”

(Stefano Spaccapietra, Editor in Chief, Journal on Data Semantics)

Grand Challenge

Semantic UnderstandingSemantic Understanding“The Grand Challenge [of semantic understanding] has become mission critical. Current solutions … won’t scale. Businesses need economic growth dependent on the web working and scaling (cost: $1 trillion/year).”

(Michael Brodie, Chief Scientist, Verizon Communications)

What is Semantic Understanding?

Understanding: “To grasp or comprehend [what’s]intended or expressed.’’

Semantics: “The meaning or the interpretation of a word, sentence, or other language form.”

- Dictionary.com

Can We Achieve Semantic Understanding?

“A computer doesn’t truly ‘understand’ anything.”

But computers can manipulate terms “in ways that are useful and meaningful to the human user.”

- Tim Berners-Lee

Key Point: it only has to be good enough.And that’s our challenge and our opportunity!

…


Information Value Chain

Meaning

Knowledge

Information

Data

Translating data into meaning

Foundational Definitions

Meaning: knowledge that is relevant or activates Knowledge: information with a degree of

certainty or community agreement Information: data in a conceptual framework Data: attribute-value pairs

- Adapted from [Meadow92]



certainty or community agreement (ontology) Information: data in a conceptual framework Data: attribute-value pairs










Data

Attribute-Value Pairs• Fundamental for information• Thus, fundamental for knowledge & meaning

Data

Attribute-Value Pairs• Fundamental for information• Thus, fundamental for knowledge & meaning

Data Frame• Extensive knowledge about a data item

�Everyday data: currency, dates, time, weights & measures

�Textual appearance, units, context, operators, I/O conversion

• Abstract data type with an extended framework


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Olympus C-750 Ultra Zoom

Sensor Resolution: 4.2 megapixelsOptical Zoom: 10 xDigital Zoom: 4 xInstalled Memory: 16 MBLens Aperture: F/8-2.8/3.7Focal Length min: 6.3 mmFocal Length max: 63.0 mm

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Sensor Resolution 4.2 megapixelsOptical Zoom 10 xDigital Zoom 4 xInstalled Memory 16 MBLens Aperture F/8-2.8/3.7Focal Length min 6.3 mmFocal Length max 63.0 mm

Digital Camera



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Year 2002Make FordModel ThunderbirdMileage 5,500 milesFeatures Red

ABS6 CD changerkeyless entry

Price $33,000Phone (916) 972-9117

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Price $33,000Phone (916) 972-9117

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Price $33,000Phone (916) 972-9117

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Price $33,000Phone (916) 972-9117

Car Advertisement



Price $33,000Phone (916) 972-9117

?

Flight # Class From Time/Date To Time/Date Stops

Delta 16 Coach JFK 6:05 pm CDG 7:35 am 0 02 01 04 03 01 04

Delta 119 Coach CDG 10:20 am JFK 1:00 pm 0 09 01 04 09 01 04

?




Airline Itinerary




?

Monday, October 13, 2003

Group A W L T GF GA Pts.USA 3 0 0 11 1 9Sweden 2 1 0 5 3 6North Korea 1 2 0 3 4 3Nigeria 0 3 0 0 11 0

Group B W L T GF GA Pts.Brazil 2 0 1 8 2 7…

?




World Cup Soccer




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Calories 250 calDistance 2.50 milesTime 23.35 minutesIncline 1.5 degreesSpeed 5.2 mphHeart Rate 125 bpm

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Treadmill Workout


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Place Bonnie LakeCounty DuchesneState UtahType LakeElevation 10,000 feetUSGS Quad Mirror LakeLatitude 40.711ºNLongitude 110.876ºW

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Maps



Information Extraction OntologiesSource Target

InformationExtraction

InformationExchange

What is an Extraction Ontology? Augmented Conceptual-Model Instance

• Object & relationship sets• Constraints• Data frame value recognizers

Robust Wrapper (Ontology-Based Wrapper)• Extracts information• Works even when site changes or when new sites

come on-line

CarAds

Color

Feature

AccessoryBodyType

OtherFeatureEngine

Transmission

Mileage

ModelTrim

TrimModel

Year

Make

Price

PhoneNr

0:1

has1:*

0:1has1:*

0:0.7:1has

1:* 0:0.9:1has

1:*

0:0.78:1

has

1:*

0:1

1:*

0:1

1:*

0:1

has1:*

0:*has

1:*

0:*

has

1:*

CarAds

Color

Feature

AccessoryBodyType

OtherFeatureEngine

Transmission

Mileage

ModelTrim

TrimModel

Year

Make

Price

PhoneNr

0:1

has1:*

0:1has1:*

0:0.7:1has

1:* 0:0.9:1has

1:*

0:0.78:1

has

1:*

0:1

1:*

0:1

1:*

0:1

has1:*

0:*has

1:*

0:*

has

1:*

CarAds Extraction Ontology

<ObjectSet x="329" y="51" lexical="true" name="Mileage" id="osmx50"> <DataFrame> <InternalRepresentation> <DataType typeName="String"/> </InternalRepresentation> <ValuePhraseList> <ValuePhrase hint="Mileage Pattern 1"> <ValueExpression color="ffffff"> <ExpressionText>[1-9]\d{0,2}[kK]</ExpressionText> </ValueExpression> <LeftContextExpression color="ffffff"> …

<ObjectSet x="329" y="51" lexical="true" name="Mileage" id="osmx50"> <DataFrame> <InternalRepresentation> <DataType typeName="String"/> </InternalRepresentation> <ValuePhraseList> <ValuePhrase hint="Mileage Pattern 1"> <ValueExpression color="ffffff"> <ExpressionText>[1-9]\d{0,2}[kK]</ExpressionText> </ValueExpression> <LeftContextExpression color="ffffff"> …

Extraction Ontologies:An Example of

Semantic Understanding

“Intelligent” Symbol Manipulation Gives the “Illusion of Understanding” Obtains Meaningful and Useful Results


A Variety of Applications Information Extraction Semantic Web Page Annotation Free-Form Semantic Web Queries Task Ontologies for Free-Form Service Requests High-Precision Classification Schema Mapping for Ontology Alignment Record Linkage Accessing the Hidden Web Ontology Discovery and Generation Challenging Applications (e.g. BioInformatics)

Application #1

Information Extraction

'97 CHEVY Cavalier, Red, 5 spd, only 7,000 miles. Previous owner heart broken! Asking only $11,995. #1415. JERRY SEINER MIDVALE, 566-3800 or 566-3888


Constant/Keyword Recognition

Descriptor/String/Position(start/end)

Year|97|2|3Make|CHEV|5|8Make|CHEVY|5|9Model|Cavalier|11|18Feature|Red|21|23Feature|5 spd|26|30Mileage|7,000|38|42KEYWORD(Mileage)|miles|44|48Price|11,995|100|105Mileage|11,995|100|105PhoneNr|566-3800|136|143PhoneNr|566-3888|148|155

Heuristics

Keyword proximity Subsumed and overlapping constants Functional relationships Nonfunctional relationships First occurrence without constraint violation


Keyword Proximity

'97 CHEVY Cavalier, Red, 5 spd, only 7,000 miles on her. Previous owner heart broken! Asking only $11,995. #1415. JERRY SEINER MIDVALE, 566-3800 or 566-3888


Subsumed/Overlapping Constants





Functional Relationships



Nonfunctional Relationships




First Occurrence without Constraint Violation





Database-Instance Generator

insert into Car values(1001, “97”, “CHEVY”, “Cavalier”, “7,000”, “11,995”, “556-3800”)insert into CarFeature values(1001, “Red”)insert into CarFeature values(1001, “5 spd”)

Application #2

Semantic Web Page Annotation

Annotated Web Page(Demo)

OWL<owl:Class rdf:ID="CarAds"> <rdfs:label xml:lang="en">CarAds</rdfs:label>...... <rdfs:subClassOf>

<owl:Restriction> <owl:onProperty rdf:resource="#hasMileage" /> <owl:minCardinality rdf:datatype="&xsd;nonNegativeInteger">0</owl:minCardinality>

</owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf>

<owl:Restriction> <owl:onProperty rdf:resource="#hasMileage" />

<owl:maxCardinality rdf:datatype="&xsd;nonNegativeInteger">1</owl:maxCardinality>

</owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="#hasMileage" /> <owl:allValuesFrom rdf:resource="#Mileage" /> </owl:Restriction> </rdfs:subClassOf>……</owl:Class>……<owl:Class rdf:ID="Mileage"> <rdfs:label xml:lang="en">Mileage</rdfs:label>……</owl:Class>……

<CarAds rdf:ID="CarAdsIns2"><CarAdsValue rdf:datatype="&xsd;string">2</CarAdsValue>

</CarAds>……<Mileage rdf:ID="MileageIns2">

<StartingCharPosition rdf:datatype="&xsd;nonNegativeInteger">237</StartingCharPosition>

<EndingCharPosition rdf:datatype="&xsd;nonNegativeInteger">241</EndingCharPosition>

</Mileage>…….<owl:Thing rdf:about="#CarAdsIns2">

<hasMake rdf:resource="#MakeIns2" /><hasModel rdf:resource="#ModelIns2" /><hasYear rdf:resource="#YearIns2" /><hasMileage rdf:resource="#MileageIns2" /><hasPhoneNr rdf:resource="#PhoneNrIns2" /><hasPrice rdf:resource="#PriceIns2" />

</owl:Thing>

……

Application #3

Free-Form Semantic Web Queries

Find Ontology“Tell me about cruises on San Francisco Bay. I’d like to know

scheduled times, cost, and the duration of cruises on Friday of next week.”

Formulate Query

Friday, Oct. 29thcost

duration

Selection Constants

San Francisco Bayscheduled times

Projection

= Result ( )

Join Path

StartTime Price Duration Source

10:45 am, 12:00 pm, 1:15, 2:30, 4:00 $20.00, $16.00, $12.00

1

10:00 am, 10:45 am, 11:15 am, 12:00 pm, 12:30 pm, 1:15 pm, 1:45 pm, 2:30 pm, 3:00 pm, 3:45 pm, 4:15 pm, 5:00 pm

$17.00, $16.00, $12.00

1 Hour 2

Application #4

Task Ontologies for Free-Form Service Requests

Basic Idea Service Request

Match with Task Ontology• Domain Ontology• Process Ontology

Complete, Negotiate, Finalize

I want to see a dermatologist next week; any day would

be ok for me, at 4:00 p.m. The dermatologist must be

within 20 miles from my home and must accept my

insurance.

Domain Ontology

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration

Medical Service Provider

Auto Service Provider Auto Mechanic

Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment …

context keywords/phrase: “appointment |want to see a |…”

Dermatologist …

context keywords/phrases: “([D|d]ermatologist) | …”




insurance.

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment …


Dermatologist …





insurance.

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment …


Dermatologist …





insurance.

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment …


Dermatologist …





insurance.

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment …


Dermatologist …





insurance.

Date …NextWeek(d1: Date, d2: Date)returns (Boolean{T,F})context keywords/phrases: next week | week from now | …

Distanceinternal representation : real;input (s: String)context keywords/phrases: miles | mile | mi | kilometers | kilometer | meters | meter | centimeter | … Within(d1: Distance, “20”)returns (Boolean {T or F})context keywords/phrases: within | not more than | | …return (d1d2)…end;

Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->Appointment

Place

Insurance

Service Provider

Person

NameDoctor

Pediatrcian

Service Description

Duration



Dermatologist

Address

Cost

Date

Time

has

is at

is on

has

provides

has

accepts

hashas

"IHC"

is with

is for

is at

is at

has

"DMBA"

is at

->

Appointment

Place

Dermatologist

Person

Name

Address

Date

Time

is at

is on

has

hasis with

is for

is at

is at

has

is at

->Appointment

Place

Dermatologist

Person

Name

Address

Date

Time

is at

is on

has

hasis with

is for

is at

is at

has

is at

->

Process Ontology

ready to schedule

task-view = null

report that the appointment cannot be scheduled

task-view != null

schedule-appointment(task-view.Person.Name,task-view.Service Provider.Name, task-view.Date, task-view.Time, task-view.Address);report that the appointment is scheduled;

initial-task-view ready

no missing information missing information

task-view = get-from-system(task-view); if (still missing values) task-view = ger-from-user(task-view);

@process ontology(domain ontology)

task-view = create-task-view(domain ontology);task-constraints = create-task-constraints(task-view);

ready@create

initialize

.

.

.

ready to schedule

task-view = null

report that the appointment cannot be scheduled

task-view != null

schedule-appointment(task-view.Person.Name,task-view.Service Provider.Name, task-view.Date, task-view.Time, task-view.Address);report that the appointment is scheduled;

initial-task-view ready

no missing information missing information

task-view = get-from-system(task-view); if (still missing values) task-view = ger-from-user(task-view);

@process ontology(domain ontology)

task-view = create-task-view(domain ontology);task-constraints = create-task-constraints(task-view);

ready@create

initialize

.

.

.

Specification Satisfaction

"Dr. Carter" "Lynn Jones"

Dermatologist0 "IHC" "DMBA"

Appointment7 "4:00""5 Jan 05"

Person100

"Orem 600 State St." "Provo 300 State St."

"Dr. Carter" "Lynn Jones"

Dermatologist0 "IHC" "DMBA"

Appointment7 "4:00""5 Jan 05"

Person100

"Orem 600 State St." "Provo 300 State St."

Date(“28 Dec 04”) and NextWeek(“28 Dec 04”, “5 Jan 05”)Dermatologist(Dermatologist0) is at Address(“Orem 600 State St.”) and Within(DistanceBetween(“Provo 300 State St.”, “Orem 600 State St.”), “22”)i2 (Dermatologist(Dermatologist0) accepts Insurance(i2) and Equal(“IHC”, i2))

Application #5

High-Precision Classification

An Extraction Ontology Solution

Document 1: Car Ads

Document 2: Items for Sale or Rent

Density Heuristic

Document 1: Car Ads

Year: 3Make: 2Model: 3Mileage: 1Price: 1Feature: 15PhoneNr: 3

Expected Values Heuristic


Year: 1Make: 0Model: 0Mileage: 1Price: 0Feature: 0PhoneNr: 4

Vector Space of Expected Values

OV ______ D1 D2Year 0.98 16 6Make 0.93 10 0Model 0.91 12 0Mileage 0.45 6 2Price 0.80 11 8Feature 2.10 29 0PhoneNr 1.15 15 11

D1: 0.996D2: 0.567

ov

D1

D2

Grouping Heuristic

YearMakeModelPriceYearModelYearMakeModelMileage…

Document 1: Car Ads

{{{

YearMileage…MileageYearPricePrice…


{{

GroupingCar Ads----------------YearYearMakeModel-------------- 3PriceYearModelYear---------------3MakeModelMileageYear---------------4ModelMileagePriceYear---------------4…Grouping: 0.875

Sale Items----------------YearYearYearMileage-------------- 2MileageYearPricePrice---------------3YearPricePriceYear---------------2PricePricePricePrice---------------1…Grouping: 0.500

Expected Number in Group = floor(∑ Ave ) = 4 (for our example)

Sum of Distinct 1-Max Object Sets in each GroupNumber of Groups * Expected Number in a Group

1-Max

3+3+4+4 4*4

= 0.875 2+3+2+1 4*4

= 0.500

Application #6

Schema Mapping forOntology Alignment

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}

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).

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


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,

Application #7

Record Linkage

“Kelly Flanagan” Query

Gather evidence from each of several different facets• Attributes• Links• Page Similarity

Combine the evidence

A Multi-faceted Approach

Phone number, email address, state, city, zip code Data-frame recognizers

Attributes

Links

“adjacent cap-word pairs”: Cap-Word (Connector | Preposition (Article)? | (Capital-LetterDot))? Cap-Word.

Page Similarity

C1 C2 ….. Ci ….. Cj … Cn

C1 1 C12 C1i C1j C1n

C2 1 C2i C2j C2n

: : : :

Ci 1 Cij Cin

: : :

Cj 1 Cjn

: :

Cn 1

P(Ci and Cj refer to a same person | evidence for a facet f )

0 if no evidence for a facet f

Cij =

Training set to compute the conditional probabilities

Confidence Matrix for Each Facet

0.96 + 0 + 0.78 - 0.96 * 0 - 0.96 * 0.78 - 0.78 * 0 + 0.96 * 0 * 0.78 = 0.9912

Confidence Matrix for Attributes Confidence Matrix for Links Confidence Matrix for Page Similarity

Final Matrix

Input: final confidence matrix Output: citations grouped by same person The idea:

{Ci , Cj} and {Cj , Ck} then {Ci , Cj , Ck}

The threshold we use for “highly confident” is 0.8.

Grouping Algorithm

Experimental Results

Application #8

Accessing the Hidden Web

Obtaining Data Behind Forms

• Web information is stored in databases

• Databases are accessed through forms

• Forms are designed in various ways

Hidden Web Extraction System

Input Analyzer

Retrieved Page(s)

User Query

Site Form

Output Analyzer

Extracted Information

ApplicationExtraction Ontology

“Find green cars costing no more than $9000.”

Application #9

Ontology Discovery & Generation

TANGO: Table Analysis for Generating Ontologies

Recognize and normalize table information Construct mini-ontologies from tables Discover inter-ontology mappings Merge mini-ontologies into a growing ontology

Recognize Table Information

Religion Population Albanian Roman Shi’a SunniCountry (July 2001 est.) Orthodox Muslim Catholic Muslim Muslim other

Afganistan 26,813,057 15% 84% 1%Albania 3,510,484 20% 70% 30%

Construct Mini-Ontology Religion Population Albanian Roman Shi’a SunniCountry (July 2001 est.) Orthodox Muslim Catholic Muslim Muslim other

Afganistan 26,813,057 15% 84% 1%Albania 3,510,484 20% 70% 30%

Discover Mappings

Merge

Application #10

Challenging Applications(e.g. BioInformatics)

Large Extraction Ontologies

Complex Semi-Structured Pages

Additional Analysis Opportunities

Sibling Page Comparison Semi-automatic Lexicon Update Seed Ontology Recognition

Sibling Page Comparison

Sibling Page ComparisonAttributes



Semi-automatic Lexicon Update

Additional Protein Names

Additional Source Speciesor Organisms

nucleus;

nucleus;zinc ion binding;nucleic acid binding;

zinc ion binding;nucleic acid binding;

linear;

NP_079345;

9606;

Eukaryota; Metazoa;Chorata;Craniata;Vertebrata;Euteleostomi;Mammalia;Eutheria;Primates;Catarrhini;Hominidae;Homo;

NP_079345;

Homo sapiens;human;

GTTTTTGTGTT……….ATAAGTGCATTAACGGCCCACATG;

FLJ14299

msdspagsnprtpessgsgsgg………tagpyyspyalygqrlasasalgyq;

hypothetical protein FLJ14299;

8;eight;

“8:?p\s?12”;“8:?p11.2”;“8:?p11.23”;:: “37,?612,?680”;

“37,?610,?585”;

Seed Ontology Recognition

Seed Ontology Recognition


Limitations and Pragmatics

Data-Rich, Narrow Domain Ambiguities ~ Context Assumptions Incompleteness ~ Implicit Information Common Sense Requirements Knowledge Prerequisites …

Busiest Airport in 2003?

Chicago - 928,735 Landings (Nat. Air Traffic Controllers Assoc.) - 931,000 Landings (Federal Aviation Admin.)Atlanta - 58,875,694 Passengers (Sep., latest numbers available)Memphis - 2,494,190 Metric Tons (Airports Council Int’l.)







Ambiguous Whom do we trust? (How do they count?)



Important qualification

Dow Jones Industrial Average

High Low Last Chg30 Indus 10527.03 10321.35 10409.85 +85.1820 Transp 3038.15 2998.60 3008.16 +9.8315 Utils 268.78 264.72 266.45 +1.7266 Stocks 3022.31 2972.94 2993.12 +19.65

44.07

10,409.85

Graphics, Icons, …

Dow Jones Industrial Average

High Low Last Chg30 Indus 10527.03 10321.35 10409.85 +85.1820 Transp 3038.15 2998.60 3008.16 +9.8315 Utils 268.78 264.72 266.45 +1.7266 Stocks 3022.31 2972.94 2993.12 +19.65

44.07

10,409.85

Reported onsame date

WeeklyDaily

Implicit information: weekly stated in upper corner of page; daily not stated.


Some Key Ideas Data, Information, and Knowledge Data Frames

• Knowledge about everyday data items• Recognizers for data in context

Ontologies• Resilient Extraction Ontologies• Shared Conceptualizations

Limitations and Pragmatics

Some Research Issues

Building a library of open source data recognizers Precisely finding and gathering relevant information

• Subparts of larger data• Scattered data (linked, factored, implied)• Data behind forms in the hidden web

Improving concept matching• Indirect matching• Calculations, unit conversions, alternative representations,

… …

Some Research Challenges

Web Page Understanding• Suppose extraction is ~85% accurate• Generate a page grammar

� Increased recall (more extracted)� Increased precision (fewer false positives)�Fast extraction from same-site sibling pages

Universal Rules for Schema Matching• Must rules be domain-specific?• Can some rules be “universal”?

Boundaries of Usefulness: When should machine learning not be used?

Application to Significant Problems• Like those above• Many more …

www.deg.byu.edu

(Machine Learning)

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Toward Tomorrow’s Semantic Web An Approach Based on Information Extraction Ontologies David W....

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