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

died in, perished in, was killed in,...

May deliver non-canonic relations ر

England, UK, Great Britain, ...

May deliver non-canonic entities ر

diedInPlace(Elvis,England)

diedInPlace(Elvis,Germany)

May deliver inconsistent facts ر


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 linear approximation algorithm ر(Johnson's) does not work well with our type of formulas

Weighted MAX SAT Problem

Johnson's cannot approximate better than 2/3

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

A v B A v C B v C


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

FMS Algorithm

diedIn

St. Elvis

FMS Algorithm


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


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 (experiments show 90% precision) ر

Conclusion

died in England... but is alive!


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 ?

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

Documents

information extraction

selforganizing framework

suchanek sofie

ontological information

pattern maps

france diedinplace

meaningful pattern

elvisengland diedinplace