sofie - a unified approach to ontology-based information extraction using reasonig

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SOFIE - A Unified Approach To Ontology-Based Information Extraction

Using Reasonig

Tobias WunnerUnit for Natural Language Processing (UNLP)

[email protected]

Wednesday,22nd June, 2011

DERI, Reading Group


Based On:

“SOFIE: A Self-Organizing Framework for Information Extraction”

Authors: Fabian Suchanek, Mauro Sozio, Gerhard Weikum

Published: World Wide Web Conference (WWW)

Madrid, 2009

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Overview

1. Introduction

2. SOFIE Model + Rules

3. Excursion: Satisfiability

4. SOFIE Approach

5. Evaluation experiments

6. Conclusion

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Motivation

Classical IE on text pattern-based 80pc

Semistructural approach Wikipedia infoboxes 95%

Idea of Paper: combine

use text (hypotheses) + ontology (trusted facts)

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Example

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Einstein attended secondary school in Germany.

Document1

YAGO ontology

familyName(AlbertEinstein, Einstein)bornIn(AlbertEinstein, Germany)

New Knowledge

attendedSchoolIn( AlbertEinstein, Germany)


General Idea

Express extraction patterns as fact

Rules to understand usage of terms

Add restrictions

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patternOcc(“X went to school in Y”,Einstein, Switzerland)

patternOcc(Pattern,X,Y) and R(X,Y) ⇒ express(Pattern,R)


Contribution

Unified approach toPattern matching

Word Sense Disambiguation

Reasoning

Large ScaleOn Unstructured Data

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Pattern extraction with WICs

Extract patterns based on ‘interesting’ entities

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Einstein was born at Ulm in Württemberg, Germany, on March 18, 1879. When Albert was around four, his father gave him a magnetic compass.

When Albert became older, he went to a school in Switzerland. After he graduated, he got a job in the patent office there…

Documents

patternOcc(“Einstein was born in Ulm”,Einstein@D1, Ulm@D1) [1]

patternOcc(“Ulm is in Württemberg, Germany”,Ulm@D1, Germany@D1) [1]patternOcc(“Albert .. Switzerland”,Albert@D1, Switzerland@D1) [1]

Knowledge Base

WICs (Word in Context)


Grounding

Test Rules How?

find an instance which satisfies the formulae

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bornIn(X,Ulm) ⇒ ¬bornIn(X,Timbuktu)

studiedIn(X,Ulm)

bornIn(Einstein,Ulm) ⇒ ¬bornIn(Einstein,Timbuktu)

studiedIn(Einstein,Ulm)


Rules (Hypotheses)

Disambiguation

– disambiguatesAs(Albert@D,AlberEinstein)[?]

Expresses a new fact

– expresses(P, livedIn(Einstein,Switzerland) )[?]

New facts

– CityIn(Ulm,Germany)[?]

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New fact rule

...with disambiguation

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patternOcc( P, WX, WY ) and

disambiguatesAs(WX, X) and

disambiguatesAs(WY, Y) and

R(X,Y)

⇒ express( P, R )

“Pattern P expresses

Relation R when

analysis of WICs

are

disambiguated”


Restrictions

Disambiguation disambiguation prior should influence

choice of disambiguation

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disambPrior( W, X, N )

⇒ disambiguatedAs( W, X ) | words(D1) ∩ rel(AlbertEinstein)|

N - any disamb. function

| words(D1) |


Restrictions

Functional restrictions

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R(X,Y) and

type(R, function) and

different(Y,Z)

⇒ ¬R(X,Z)

Albert@D1 ≠ Albert@D2

“Albert@D1 born in?”


SOFIE Rules

Framework to test the hypotheses Question

“How to satisfy all them?”

rules + trusted facts

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patternOcc( P, X, Y ) and

R(X,Y)

⇒ express( P, R )

dismbPrior(Albert@D1, HermannEinstein, 3)

⇒ disambiguatesAs(Albert@D1,

HermannEinstein)dismbPrior(Albert@D1, AlbertEinstein, 10)

⇒ disambiguatesAs(Albert@D1, AlbertEinstein)

Country(Germany)

livedIn(AlbertEinstein,Ulm)

…


SAT / MAX SAT

SAT (Satisfiability) proove formula can be TRUE

Complexity Classes P Good example: Nk

NP Bad cN

– e.g. naive algorithm for 100 variables

2100 x 10-10 ms per row = 4 x 1012 y

– Not always.. 3SAT in (4/3)N

– SAT Solver

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X Y Z F

0 0 0 0

0 0 1 1

0 1 0 1

0 1 1 0

1 0 0 0

1 0 1 1

1 1 0 1

1 1 1 0

truth table has 23 rows

F = (X or Y or Z) and (¬X or Y or Z)

and (¬X or ¬Y or ¬Z)G = (X or Y) and (¬X or ¬Y) and (X)

Details Schöning 2010


SAT / MAX SAT

SAT (Satisfiability) proove formula can be TRUE

Complexity Classes P Good example: Nk

NP Bad cN

– e.g. naive algorithm for 100 variables

2100 x 10-10 ms per row = 4 x 1012 y

– Not always.. 3SAT in (4/3)N

– SAT Solver

MAX SAT

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F = (X or Y or Z) and (¬X or Y or Z)

and (¬X or ¬Y or ¬Z)G = (X or Y) and (¬X or ¬Y) and (X)

X Y Z F

0 0 0 0

0 0 1 1

0 1 0 1

0 1 1 0

1 0 0 0

1 0 1 1

1 1 0 1

1 1 1 0

truth table has 23 rows

X Y G #clauses

0 0 0 1

0 1 0 2

1 0 0 3

1 1 0 2

Details Schöning 2010


Weighted MAX SAT in SOFIE

...back to SOFIE

this is MAX SAT but with weights

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patternOcc( P, X, Y ) and

R(X,Y)

⇒ express( P, R )

dismbPrior(Albert@D1, HermannEinstein, 3)

⇒ disambiguatesAs(Albert@D1, HermannEinstein)

dismbPrior(Albert@D1, AlbertEinstein, 10)

⇒ disambiguatesAs(Albert@D1, AlbertEinstein)

Country(Germany)

livedIn(AlbertEinstein,Ulm)

…

rules + trusted facts



Weighted MAX SAT is NP hard only approximation algorithms

impractical to find optimal solution

SAT Solver Johnson’s algorithm: 2/3 (apprx guarantee)



Functional MAX SAT

Specialized reasoning (support for functional properties)

Approximation guarantee 1/2

A v B [w1]

A v B [w2]

B v C [w3]

C [w4]

Considers only unit clauses

Propagates dominating unit clauses

A v B [10]

A [10]

A [30]

A = true

30 > 10+10


Controlled experiment

Corpus from Wikipedia infoboxes 100 articles

Semantic is known!

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Controlled experiment

Large-scale: Corpus from Wikipedia articles 2000 articles

13 frequent relations from YAGO

Parsing = 87min Reaoning = 77min

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Unstructured text sources

150 news paper articles relation under test headquarterOf

YAGO (modified with relation seeds)

Parsing 87min WeightedMaxSat 77min

disambiguated entries (provenance) could be manually assessed

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functionalrelation


Unstructured text sources

Large-scale: 10 biographies for each of 400 US senators

5 relationships

Disambiguation was not ideal for YAGO (13 James Watson)

Parsing 7h W-MAX-SAT 9h

Results

– 4 good

– 1 bad (misleading patterns)

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Reformulate OWL in propositional logic OWL FOL Skolem Normal Form Propositional Logic

Might find OWL-inconsistent ontologies due to OW Assumption

MAX SAT can’t do OWL per se (Open World Assumption)

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define a student as a subclass “attends some course”

⇒ ∀ x, ∃ y: attends(x,y), Course(y) → Student(y)

⇒ ∀ x: attends(x,k), Course(y) → Student(y); ∃ k

⇒ ¬attends(xi, ki) or ¬Course(xi) or Student(xi); k=x1 .. xn

Inferred Ontology

{ Student(alex), Student(bob), Student subClassOf attends some Course, attends(alex, SemanticWeb) }

Details JMC 2010


Conclusions

Ontology-based IE (OBIE) reformulated as

weighted MAX SAT problem

Approximation algorithm with 1/2

Works and scales (large corpus + YAGO)

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Limitations

Specialized approximation algorithm– Accounts for SOFIE rules NOT OWL

MAX SAT Restrictions∈ Prepositional Logic

∉ First-Order Logic

Ontology population approach (can’t infer new relations)

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References

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1. F Suchanek et al, SOFIE: a self-organizing framework for information extraction, Proceeding WWW '09 Proceedings of the 18th international conference on World wide web, link

2. John McCrae, Automatic Extraction Of Logically Consistent Ontologies From Text, PhD thesis at National Institute of Informatics, Japan, 2009 link

3. Uwe Schöning: Das SAT-Problem. In Informatik Spektrum 33(5): 479-483, 2010, link

4. F Suchanek, Automated Construction and Growth of a Large Ontology, PhD thesis at Technology of Saarland University. Saarbrücken, Germany, 2009, link

http://delivery.acm.org/10.1145/1530000/1526794/p631-suchanek.pdf?ip=140.203.154.5&CFID=27879704&CFTOKEN=23280686&__acm__=1308694210_348e803d81ef727615ad853153952f72

http://www.sc.cit-ec.uni-bielefeld.de/sites/www.sc.cit-ec.uni-bielefeld.de/files/thesis.pdf

http://www.springerlink.com/content/4560q13110433285/

http://suchanek.name/work/publications/submitted.pdf

sofie - a unified approach to ontology-based information extraction using reasonig

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