© Jesse Davis 2006 View Learning Extended: Learning New Tables Jesse Davis 1, Elizabeth Burnside 1, David Page 1, Vítor Santos Costa 2 1 University of

© Jesse Davis 2006© Jesse Davis 2006

View Learning Extended:View Learning Extended:Learning New TablesLearning New Tables

Jesse DavisJesse Davis11, Elizabeth Burnside, Elizabeth Burnside11, , David PageDavid Page11, Vítor Santos Costa, Vítor Santos Costa22

11University of Wisconsin-MadisonUniversity of Wisconsin-MadisonUSAUSA

22Federal University of Rio de JaneiroFederal University of Rio de JaneiroBrasilBrasil


1 P1 5/02 No 0.03 RU4 B

2 P1 5/04 Yes 0.05 RU4 M

3 P1 5/04 No 0.04 LL3 B

4 P2 6/00 No 0.02 RL2 B … … … … … … …

Abnormality Patient Date Calcification … Mass Loc Benign/ Fine/Linear Size Malignant

View Learning FrameworkView Learning Framework[Davis et al. IJCAI05][Davis et al. IJCAI05]

Learn fields predictive of Learn fields predictive of target concepttarget concept


1 P1 5/02 No 0.03 No RU4 B

2 P1 5/04 Yes 0.05 Yes RU4 M

3 P1 5/04 No 0.04 No LL3 B

4 P2 6/00 No 0.02 No RL2 B … … … … … … … …

Abnormality Patient Date Calcification … Mass Increase Loc Benign/ Fine/Linear Size in size Malignant

Extend SchemaExtend Schema

IncreaseIn Size

No

Yes

No

No…


Integrated Search for New Integrated Search for New FieldsFields

[Landwehr et al. AAAI 2005, Davis et al. ECML 2005][Landwehr et al. AAAI 2005, Davis et al. ECML 2005]

Old approach: Old approach: Step 1 use ILP to learn new fieldsStep 1 use ILP to learn new fields Step 2 learn statistical modelStep 2 learn statistical model

Score As You Use (SAYU):Score As You Use (SAYU): Combine steps 1 and 2Combine steps 1 and 2 Score new field by how much it helps statistical Score new field by how much it helps statistical

modelmodel

Parallel development: nFOIL Parallel development: nFOIL


Relevant Intermediate Relevant Intermediate ConceptsConcepts

Advisedby(Student,Professor)

ta_for(Student,Professor)

ta(Student,Class) teach(Professor,Class)

coauthor(Person,Person)paper(Person,Ref)

Goal: Automatically generate and Automatically generate and incorporate intermediate conceptsincorporate intermediate concepts


Limitations to Our Old WorkLimitations to Our Old Work

Previously View Learning adds new Previously View Learning adds new fieldsfields

More expressive to learn predicates More expressive to learn predicates not approximations to target conceptnot approximations to target concept represent new tablesrepresent new tables

Solution: Extend SAYUSolution: Extend SAYU


VISTAVISTA Algorithm Algorithm

VView iew

IInvention throughnvention through

SScoring coring

TTables withables with

AAggregationggregation


Distinguished types [id, patient, visit]

Algorithm IllustrationAlgorithm Illustration

p1(id,id)

p1/2

Rule 14 Rule N

ClassValue

…Score = 0.0

20.12

0.10

0.15

0.35

Rule 1Rule 2Rule 3

p2/1

p2(patient)

:-sameStudy(Id1,Id2):-historyOfBC(Patient):-hadBiopsy(_,Patient)

BackgroundKnowledge


Algorithm Details Algorithm Details

Learn predicates withLearn predicates with Target predicate arityTarget predicate arity Target predicate arity + 1Target predicate arity + 1

Moded language Moded language

Breadth first search over clause bodiesBreadth first search over clause bodies


Count AggregationCount Aggregation

1

2

3

4

5

6

…

Id Count

density_increase(density_increase(AA,,BB) :- density() :- density(AA,,D1D1),), prior_mammogram_same_loc(prior_mammogram_same_loc(AA,,BB), ), density(density(BB,,D2D2), ), D1D1 > > D2D2. .

0

1

0

0

1

2

…

Count

1 P1 5/02 low RU4 B

2 P1 5/04 high RU4 M

3 P1 5/04 none LL3 B

4 P2 6/00 none RL2 B

5 P2 6/02 low RL2 B

6 P2 9/03 high RL2 M

… … … … … …

Id Patient Date … Mass Loc Benign/ Density Malignant


LinkageLinkage

Distinguished variable may not correspond Distinguished variable may not correspond to example keyto example key

p1(p1(PatientPatient) :-) :-

historyOfBC(historyOfBC(PatientPatient), hadBiopsy(), hadBiopsy(PatientPatient).).

Above rule adds a field to Patient tableAbove rule adds a field to Patient table

Q: How do we score p1?Q: How do we score p1?


Linkage ExampleLinkage Example

P1 No

P2 Yes

P3 No

P4 No

P5 Yes

P6 No

…

Patient Family History

p1(p1(PatientPatient) :- historyOfBC() :- historyOfBC(PatientPatient), ), hadBiopsy(_, hadBiopsy(_,PatientPatient).).

1 P1 5/02 low RU4 B




5 P2 6/02 low RL2 B


… … … … … …



1 P1 5/02 low RU4 B




5 P2 6/02 low RL2 B


… … … … … …


P1 No No

P2 Yes Yes

P3 No No

P4 No No

P5 Yes No

P6 No No

… … …

Patient Family p1 History






1 P1 5/02 low RU4 B No

2 P1 5/04 high RU4 M No

3 P1 5/04 none LL3 B No

4 P2 6/00 none RL2 B Yes

5 P3 6/02 low RL2 B Yes

6 P4 9/03 high RL2 M Yes

… … … … … … …

Id Patient Date … Mass Loc Benign/ p1 Density Malignant


New Features in VISTANew Features in VISTA

User declares set of User declares set of distinguished typesdistinguished types that appear in clause headthat appear in clause head

Allow Allow reuse of learned predicatereuse of learned predicate

Count aggregationCount aggregation

Linkage permits learning predicates with:Linkage permits learning predicates with: Higher arity than target (new tables)Higher arity than target (new tables) Different types than target Different types than target


ExperimentExperiment

Q: Does VISTA or SAYU perform better?Q: Does VISTA or SAYU perform better?


DatasetsDatasets

Cora (5 x 2 fold cross validation)Cora (5 x 2 fold cross validation)[McCallum et al. 00, Kok & Domingos 05][McCallum et al. 00, Kok & Domingos 05]

UW-CSE (5 fold cross validation)UW-CSE (5 fold cross validation)[Richardson & Domingos 04][Richardson & Domingos 04]

Mammography (10 fold cross Mammography (10 fold cross validation)validation)[Davis et al. 05][Davis et al. 05]


Area Under Precision-Recall Area Under Precision-Recall CurveCurve

Generate wholeGenerate wholePR CurvePR Curve

Area Under PR for Area Under PR for Recall > 0.5Recall > 0.5P

reci

sion

Recall


Cora

00.10.20.30.40.50.60.70.80.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Recall

Pre

cisi

on

VISTA

SAYU


UW-CSE

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Recall

Pre

cisi

on

VISTA

SAYU


Mammography

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Recall

Pre

cisi

on

VISTA

SAYU


UW-CSE

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Av

era

ge

Are

a U

nd

er

PR

Cu

rve VISTA

SAYUMLN

MLN data from Singla & Domingos AAAI 2005


Related Topic: Predicate Related Topic: Predicate InventionInvention

Cigol: Muggleton & Buntine (1988)Cigol: Muggleton & Buntine (1988)

CHILLIN: Zelle & Mooney (1994)CHILLIN: Zelle & Mooney (1994)

FOIL-PILFS: Craven & Slattery (2001)FOIL-PILFS: Craven & Slattery (2001)

SLR: Popescul & Ungar (2004)SLR: Popescul & Ungar (2004)


Related Work: Feature Related Work: Feature ConstructionConstruction

Pompe & Kononenko, ILP’95Pompe & Kononenko, ILP’95

Srinivasan & King, ILP’97Srinivasan & King, ILP’97

Perlich & Provost, KDD’03Perlich & Provost, KDD’03

Knobbe, de Haas & Siebes, PKDD’01Knobbe, de Haas & Siebes, PKDD’01


Future WorkFuture Work

Further investigate benefits of VISTAFurther investigate benefits of VISTA Linkage as jumping deeper into search spaceLinkage as jumping deeper into search space Reuse of predicates Reuse of predicates

Extensions to VISTAExtensions to VISTA NegationNegation DisjunctionDisjunction Stochastic searchStochastic search

Comparisons to other SRL systemsComparisons to other SRL systems


ConclusionsConclusions

VISTAVISTA adds capabilities adds capabilities Add fields to tables other than target relationAdd fields to tables other than target relation Learn new relationsLearn new relations

VISTAVISTA empirically empirically Better Cora (p-value < 0.001)Better Cora (p-value < 0.001) Almost better on UW-CSE (p-value < 0.06)Almost better on UW-CSE (p-value < 0.06) No worse on Mammography (p-value < 0.94) No worse on Mammography (p-value < 0.94)


AcknowledgementsAcknowledgements

Mark CravenMark Craven Jude ShavlikJude Shavlik Inês DutraInês Dutra Mark GoadrichMark Goadrich Irene OngIrene Ong Trevor WalkerTrevor Walker

Raghu Raghu RamakrishnanRamakrishnan

Rich MaclinRich Maclin Lisa TorreyLisa Torrey Jan StruyfJan Struyf Allison HollowayAllison Holloway

This work was partially supported by Air Force grant F30602-01-2-0571


Thank You!Thank You!

Questions?Questions?

Documents

© Jesse Davis 2006 View Learning Extended: Learning New Tables Jesse Davis 1, Elizabeth Burnside 1, David Page 1, Vítor Santos Costa 2 1 University of