fabian m. suchanek sofie: a self-organizing framework for information extraction 1 sofie: a...

SOFIE: A Self-Organizing Framework for Information Extraction 1Fabian M. Suchanek

SOFIE:A Self-Organizing

Frameworkfor Information Extraction

Fabian M. Suchanek, Mauro Sozio, Gerhard Weikum

(Max-Planck-Institute for Informatics, Saarbrücken, Germany)


Ontologies

SingerCountry

USA

Entity

bornInPlace

typetype

subclassOfsubclassOf

Wikipedia

DBpedia,

YAGO,

KYLIN,

...

Internet

?"Elvis died in England"

birth-place: USA


Information Extraction

EnglanddiedInPlace

"Elvis died in England"

Previous approaches:

Espresso, DIPRE, LEILA, Snowball, TextRunner, Alice, and many more

Goal:

Extract ontological information from natural language documents

May deliver non-canonic relations ر

May deliver non-canonic entities ر

May deliver inconsistent facts ر

recoverWithout(most_people, medication)

areUnder(0%, the_age_of_18)

support(these_findings, the_notion)

died in, perished in, was killed in

England, UK, Great Britain

diedInPlace(Elvis, England)diedInPlace(Elvis, Germany)

SOFIE aims to solve these problems in a new unified framework


Pitfalls of Information Extraction

Elvis died in England.

OntologyWeb page

Louis XIV died in France.

FrancediedInPlace

If a pattern occurs with two entities that stand in a relation, then the pattern maps to the relation.

"died in" = diedInPlace




OntologyWeb page




If a meaningful pattern occurs with two entities, then the entities stand in the relation.

"Elvis"

"England"diedInPlace




OntologyWeb page





"Elvis"


Taxidophobist?




Web page





"Elvis"


Taxidophobist

Reasoning Problem




Web page





Taxidophobist

Reasoning Problem

Disambiguation Problem



Elvis died in England.Louis XIV died in France.

Taxidophobist

Reasoning Problem


Pattern Matching Problem

"died in" = diedInPlace ?


Information Extraction as Formulas

type(Elvis,Taxidophobist).

type(X,Taxidophobist)

& bornInPlace(X,Y)

=> diedInPlace(X,Z) [0.8]

Taxidophobist

Reasoning Problem




Reasoning Problem





& bornInPlace(X,Y)

=> diedInPlace(X,Z)



Assumptions:

In one document, the same word has always the same meaning ر

The ontology already knows all important meanings of proper رnames

possibleMeaning(Elvis@D15, ElvisPresley). [0.7]




Assumptions:




A word in context (wic).Here: The word "Elvis"

in document D15

One possible meaning of "Elvis" as given by the ontology

Prior estimation for the likelihood of this meaning.


| words(D15) ∩ rel(ElvisPresley)|

| words(D15) |


Assumptions:





possibleMeaning(X,Y) => means(X,Y)

means(X,Y) & YZ => means(X,Z)




Reasoning Problem





& bornInPlace(X,Y)

=> diedInPlace(X,Z)


meaning(Elvis@D15,

ElvisPresley). [0.7]






occurs("died in",

Elvis@D15,

England@D15). [14]

occurs(P,Wic1,Wic2) & means(Wic1,X) & means(Wic2,Y) & mapsTo(P,R)

=> R(X,Y)

occurs(P,Wic1,Wic2) & means(Wic1,X) & means(Wic2,Y) & R(X,Y)

=> mapsTo(P,R)



Reasoning Problem




& bornInPlace(X,Y)

=> diedInPlace(X,Z)


meaning(Elvis@D15,

ElvisPresley). [0.7]

occurs("died in",

Elvis@D15,

England@D15). [14]

Find truth assignments to hypotheses so that the weight of satisfied formulas is maximized

means(Elvis@D15, ElvisPresley) ?

mapsTo("died In", diedInPlace) ?

diedIn(ElvisPresley, England) ?


Weighted MAX SAT Problem


Problems:

The Weighted MAX SAT Problem is NP-hard ر

Our instance of the problem is huge ر

The most popular greedy approximation algorithm ر(Johnson's) does not work well with our type of formulas

Weighted MAX SAT Problem

Johnson's has upper bound 2/3 on

approximation

bornInPlace(X,Y) => bornInPlace(X,Z)

A v B A v C B v C

Structurally much simpler than MLNs. No need to

model probabilities if we're just interested in the

maximum.


A v B [w1]

A v B [w2]

B v C [w3]

C [w4]

Formulas

A

B

C

Hypotheses

The Functional MAX SAT Algorithm considers only unit clauses.

= true

= false

= false

FMS Algorithm

The Functional MAX SAT Algorithm propagates Dominating Unit Clauses

A v B [10]

A [10]

A [30]

A = true30 > 10+10


FMS Algorithm

Experiments show better performance in practice than Johnson's algorithm

in our setting .

FMS Algorithm

FOR i=1 TO 42...NEXT i

Approximation

Guarantee

Polynomial

time


FMS Algorithm


FMS Algorithm

Elvis died in England r(X,Y) & s(Y) => t(X,Y)


England

FMS Algorithm

diedIn

St. Elvis

FMS Algorithm


Elvis died in England

type(Elvis,Taxidophobist)=1diedIn(Elvis,England)=0means(Elvis@D15,Elvis)=0means(Elvis@D15,...)=1

r(X,Y) & s(Y) => t(X,Y)


England

SOFIE

diedIn

St. Elvis

r(X,Y) & s(Y) => t(X,Y)


Corpus Type # Docs Relations Time Precision

Wikipedia toy corpus structured 100 3 2min 100%

Wikipedia subcorpus

semi-structured 2000 15 15h 94%

News article toy corpus unstructured 150 1 24min 91%

Biographies from Web unstructured 3440 5 15h 90%

Other Experiments

(All experiments with the YAGO ontology)


SOFIE unifies the tasks of

entity disambiguation ر

pattern extraction ر

semantic constraint reasoning ر

in a single framework, delivering

canonicalized facts ر

of high precision ر

Conclusion

died in England... but is alive!

http://mpii.de/yago-naga

s(Y) => t(X)


occurs(P,WX,WY)

/\ refersTo(WX.X)

/\ refersTo(WY,Y)

/\ R(X,Y)

=> expresses(P,R)

occurs(P,WX,WY)

/\ expressed(P,R)

/\ refersTo(WX.X)

/\ refersTo(WY,Y)

/\ range(R,D1)

/\ domain(R,D2)

/\ type(X,D1)

/\ type(Y,D2)

=> R(X,Y) R(X,Y)

R(X,Y)

/\ R(X,Z)

/\ type(R,function)

=> Y = Z

disambiguationPrior(W,X) => refersTo(W,X)

bornInYear(X,B) /\ diedInYear(X,D) => B<D

SOFIE rules!


SOFIE: Experiments

Corpus Type # Docs Relations Time Precision Recall

Wikipedia toy corpus structured 100 3 8min 100% 80%

Wikipedia toy corpus

semi-structured 50% infoboxes removed 100 3 8min 100% 57%

Wikipedia subcorpus semi-structured 2000 15 15h 94% ?

News article toy corpus unstructured 150 1 24min 91% 24%, 31%

Snowball 56% 31%

Biographies from Web unstructured 3440 5 15h 90% ?


SOFIE: Large-Scale Experiment

Goal:

Extract bornIn, bornOnDate, diedIn, diedOnDate, politicianOf

Corpus:

3700 biography documents downloaded from the Web

Runtime: (summed over 5 batches)

Parsing 7:05h

Hypothesis Generation 6:15h

Solving 2:30h

Total 15:50h

Results: (precision in %)

bornIn bornOnD diedIn diedOnD polOf

87 87 13 98 95 90


SOFIE: Relation to Markov Logic

P

bornIn(Nicholas, Patras)

false true

P(X) ~ e sat(i,X) wi

Number of satisfied instances of the ith formula

Weight of the ith formula

r(x,y) /\ s(x,z) => t(x,z) [w]

...

max X e sat(i,X) wi

max X log( e sat(i,X) wi )

max X sat(i,X) wi

~~~~> Weighted MAX SAT problem


Grounding

r(X,Y) & s(Y) => t(X,Y)

{ r(X,Y), s(Y), t(X,Y) }

{ r(a,a), s(a), t(a,a) }

{ r(a,b), s(b), t(a,b) }

{ r(b,a), s(a), t(b,a) }

{ r(b,b), s(b), t(b,b) }

r(a,a)

r(a,b)

r(b,a)

r(b,b)

Immutable, complete facts (e.g. pattern occurrences)

Entities={a,b}


Grounding

r(X,Y) & s(Y) => t(X,Y)

{ r(X,Y), s(Y), t(X,Y) }

{ s(a), t(a,a) } [w]

r(a,a) [w]

r(a,b)

r(b,a)

r(b,b)

Immutable, complete facts (e.g. pattern occurrences)


Grounding

{ s(a), t(a,a) } [w1]

{p(c,d), q(e), } [w2]


means(Elvis@D15, ElvisPresley) = true ?

mapsTo("died In", diedInPlace) = true ?

diedIn(ElvisPresley, England) = true ?

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