1 learning the structure of markov logic networks stanley kok & pedro domingos dept. of computer...

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Learning the Structure of Markov Logic

Networks

Stanley Kok & Pedro Domingos

Dept. of Computer Science and Eng.

University of Washington

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Overview Motivation Background Structure Learning Algorithm Experiments Future Work & Conclusion

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Motivation Statistical Relational Learning (SRL)

combines the benefits of: Statistical Learning: uses probability to handle

uncertainty in a robust and principled way Relational Learning: models domains with

multiple relations

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Motivation Many SRL approaches combine a logical

language and Bayesian networks e.g. Probabilistic Relational Models

[Friedman et al., 1999]

The need to avoid cycles in Bayesian networks causes many difficulties [Taskar et al., 2002]

Started using Markov networks instead

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Motivation Relational Markov Networks [Taskar et al., 2002]

conjunctive database queries + Markov networks Require space exponential in the size of the cliques

Markov Logic Networks [Richardson & Domingos, 2004]

First-order logic + Markov networks Compactly represent large cliques Did not learn structure (used external ILP system)

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Motivation Relational Markov Networks [Taskar et al., 2002]

conjunctive database queries + Markov networks Require space exponential in the size of the cliques

Markov Logic Networks [Richardson & Domingos, 2004]

First-order logic + Markov networks Compactly represent large cliques Did not learn structure (used external ILP system)

This paper develops a fast algorithm that learns MLN structure Most powerful SRL learner to date

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Overview Motivation Background Structure Learning Algorithm Experiments Future Work & Conclusion

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Markov Logic Networks

First-order KB: set of hard constraints Violate one formula, a world has zero probability

MLNs soften constraints OK to violate formulas The fewer formulas a world violates,

the more probable it is Gives each formula a weight,

reflects how strong a constraint it is

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MLN Definition A Markov Logic Network (MLN) is a set of

pairs (F, w) where F is a formula in first-order logic w is a real number

Together with a finite set of constants,it defines a Markov network with One node for each grounding of each predicate

in the MLN One feature for each grounding of each formula F

in the MLN, with the corresponding weight w

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Ground Markov Network

Student(STAN)

Professor(PEDRO)

AdvisedBy(STAN,PEDRO)

Professor(STAN)

Student(PEDRO)

AdvisedBy(PEDRO,STAN)

AdvisedBy(STAN,STAN)

AdvisedBy(PEDRO,PEDRO)

AdvisedBy(S,P) ) Student(S) ^ Professor(P)2.7

constants: STAN, PEDRO

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MLN Model

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MLN Model

Vector of value assignments to ground predicates

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MLN Model

Vector of value assignments to ground predicates

Partition function. Sums over all possiblevalue assignments to ground predicates

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MLN Model

Vector of value assignments to ground predicates

Partition function. Sums over all possiblevalue assignments to ground predicates

Weight of ith formula

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MLN Model

Vector of value assignments to ground predicates

Partition function. Sums over all possiblevalue assignments to ground predicates

Weight of ith formula

# of true groundings of ith formula

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MLN Weight Learning

Likelihood is concave function of weights Quasi-Newton methods to find optimal weights

e.g. L-BFGS [Liu & Nocedal, 1989]

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MLN Weight Learning

Likelihood is concave function of weights Quasi-Newton methods to find optimal weights

e.g. L-BFGS [Liu & Nocedal, 1989]

SLOW#P-complete

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MLN Weight Learning

Likelihood is concave function of weights Quasi-Newton methods to find optimal weights

e.g. L-BFGS [Liu & Nocedal, 1989]

SLOW#P-completeSLOW

#P-complete

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MLN Weight Learning R&D used pseudo-likelihood [Besag, 1975]

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MLN Weight Learning R&D used pseudo-likelihood [Besag, 1975]

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MLN Structure Learning

R&D “learned” MLN structure in two disjoint steps: Learn first-order clauses with an off-the-shelf

ILP system (CLAUDIEN [De Raedt & Dehaspe, 1997]) Learn clause weights by optimizing

pseudo-likelihood Unlikely to give best results because CLAUDIEN

find clauses that hold with some accuracy/frequency in the data

don’t find clauses that maximize data’s (pseudo-)likelihood

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Overview Motivation Background Structure Learning Algorithm Experiments Future Work & Conclusion

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This paper develops an algorithm that: Learns first-order clauses by directly optimizing

pseudo-likelihood Is fast enough Performs better than R&D, pure ILP,

purely KB and purely probabilistic approaches

MLN Structure Learning

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Structure Learning Algorithm

High-level algorithmREPEAT

MLN Ã MLN [ FindBestClauses(MLN)UNTIL FindBestClauses(MLN) returns NULL

FindBestClauses(MLN)Create candidate clausesFOR EACH candidate clause c

Compute increase in evaluation measureof adding c to MLN

RETURN k clauses with greatest increase

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Structure Learning Evaluation measure Clause construction operators Search strategies Speedup techniques

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Evaluation Measure

R&D used pseudo-log-likelihood

This gives undue weight to predicates with large # of groundings

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Weighted pseudo-log-likelihood (WPLL)

Gaussian weight prior Structure prior

Evaluation Measure

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Weighted pseudo-log-likelihood (WPLL)

Gaussian weight prior Structure prior

Evaluation Measure

weight given to predicate r

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Weighted pseudo-log-likelihood (WPLL)

Gaussian weight prior Structure prior

Evaluation Measure

sums over groundings of predicate r

weight given to predicate r

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Weighted pseudo-log-likelihood (WPLL)

Gaussian weight prior Structure prior

Evaluation Measure

sums over groundings of predicate r

weight given to predicate r CLL: conditional log-likelihood

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Clause Construction Operators Add a literal (negative/positive) Remove a literal Flip signs of literals Limit # of distinct variables to restrict

search space

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Beam Search

Same as that used in ILP & rule induction Repeatedly find the single best clause

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Shortest-First Search (SFS)

1. Start from empty or hand-coded MLN2. FOR L Ã 1 TO MAX_LENGTH3. Apply each literal addition & deletion to

each clause to create clauses of length L4. Repeatedly add K best clauses of length L

to the MLN until no clause of length L improves WPLL

Similar to Della Pietra et al. (1997), McCallum (2003)

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Speedup Techniques FindBestClauses(MLN)

Creates candidate clausesFOR EACH candidate clause c

Compute increase in WPLL (using L-BFGS) of adding c to MLN

RETURN k clauses with greatest increase

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Speedup Techniques FindBestClauses(MLN)

Creates candidate clausesFOR EACH candidate clause c

Compute increase in WPLL (using L-BFGS)of adding c to MLN

RETURN k clauses with greatest increase

SLOWMany candidates

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Speedup Techniques FindBestClauses(MLN)

Creates candidate clausesFOR EACH candidate clause c

Compute increase in WPLL (using L-BFGS)of adding c to MLN

RETURN k clauses with greatest increase

SLOWMany candidates

SLOWMany CLLs

SLOWEach CLL involves a#P-complete problem

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Speedup Techniques FindBestClauses(MLN)

Creates candidate clausesFOR EACH candidate clause c

Compute increase in WPLL (using L-BFGS)of adding c to MLN

RETURN k clauses with greatest increase

SLOWMany candidates

NOT THAT FAST

SLOWMany CLLs

SLOWEach CLL involves a#P-complete problem

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Speedup Techniques Clause Sampling Predicate Sampling Avoid Redundancy Loose Convergence Thresholds Ignore Unrelated Clauses Weight Thresholding

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Overview Motivation Background Structure Learning Algorithm Experiments Future Work & Conclusion

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Experiments UW-CSE domain

22 predicates, e.g., AdvisedBy(X,Y), Student(X), etc. 10 types, e.g., Person, Course, Quarter, etc. # ground predicates ¼ 4 million # true ground predicates ¼ 3000 Handcrafted KB with 94 formulas

Each student has at most one advisor If a student is an author of a paper, so is her advisor

Cora domain Computer science research papers Collective deduplication of author, venue, title

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Systems

MLN(SLB): structure learning with beam searchMLN(SLS): structure learning with SFS

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Systems

MLN(SLB) MLN(SLS)

KB: hand-coded KBCL: CLAUDIENFO: FOILAL: Aleph

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Systems

MLN(SLB) MLN(SLS)

KBCLFOAL

MLN(KB)MLN(CL)MLN(FO)MLN(AL)

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Systems

MLN(SLB) MLN(SLS)

NB: Naïve Bayes

BN: Bayesian

networks

KBCLFOAL

MLN(KB)MLN(CL)MLN(FO)MLN(AL)

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Methodology UW-CSE domain

DB divided into 5 areas: AI, Graphics, Languages, Systems, Theory

Leave-one-out testing by area Measured

average CLL of the ground predicates average area under the precision-recall curve

of the ground predicates (AUC)

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0.5330.472

0.306

0.140 0.148

0.429

0.1700.131 0.117

0.266

0.0

0.3

0.6

UW-CSE-0.061 -0.088

-0.151-0.208 -0.223

-0.142

-0.574-0.661

-0.579

-0.812

-1.0

-0.5

0.0M

LN(S

LS)

MLN

(SLB

)

MLN

(CL)

MLN

(FO

)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

CLL

AU

C

MLN

(SLS

)M

LN(S

LB)

MLN

(CL)

MLN

(FO)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

UW-CSE

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0.5330.472

0.306

0.140 0.148

0.429

0.1700.131 0.117

0.266

0.0

0.3

0.6

UW-CSE-0.061 -0.088

-0.151-0.208 -0.223

-0.142

-0.574-0.661

-0.579

-0.812

-1.0

-0.5

0.0M

LN(S

LS)

MLN

(SLB

)

MLN

(CL)

MLN

(FO

)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

CLL

AU

C

MLN

(SLS

)M

LN(S

LB)

MLN

(CL)

MLN

(FO)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

UW-CSE

53

0.5330.472

0.306

0.140 0.148

0.429

0.1700.131 0.117

0.266

0.0

0.3

0.6

UW-CSE-0.061 -0.088

-0.151-0.208 -0.223

-0.142

-0.574-0.661

-0.579

-0.812

-1.0

-0.5

0.0M

LN(S

LS)

MLN

(SLB

)

MLN

(CL)

MLN

(FO

)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

CLL

AU

C

MLN

(SLS

)M

LN(S

LB)

MLN

(CL)

MLN

(FO)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

UW-CSE

54

0.5330.472

0.306

0.140 0.148

0.429

0.1700.131 0.117

0.266

0.0

0.3

0.6

UW-CSE-0.061 -0.088

-0.151-0.208 -0.223

-0.142

-0.574-0.661

-0.579

-0.812

-1.0

-0.5

0.0M

LN(S

LS)

MLN

(SLB

)

MLN

(CL)

MLN

(FO

)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

CLL

AU

C

MLN

(SLS

)M

LN(S

LB)

MLN

(CL)

MLN

(FO)

MLN

(AL)

MLN

(KB)

CL

FO AL

KB

UW-CSE

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0.533

0.472

0.390 0.397

0.0

0.3

0.6

UW-CSE-0.061 -0.088

-0.370

-0.166

-1.0

-0.5

0.0

CLL

AU

C

MLN

(SLS

)

MLN

(SLB

)

NB

BN

MLN

(SLS

)

MLN

(SLB

)

NB

BN

UW-CSE

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Timing MLN(SLS) on UW-CSE

Cluster of 15 dual-CPUs 2.8 GHz Pentium 4 machines

Without speedups: did not finish in 24 hrs With speedups: 5.3 hrs

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4.0

21.6

8.46.5

4.1

24.8

0.0

10.0

20.0

30.0

Lesion Study Disable one speedup technique at a time; SFS

UW-CSE (one-fold)

Hour

all speedups

no clausesampling

no predicatesampling

don’t avoidredundancy

no looseconverg.

threshold

no weight thresholding

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Overview Motivation Background Structure Learning Algorithm Experiments Future Work & Conclusion

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Future Work Speed up counting of # true

groundings of clause Probabilistically bound the loss in

accuracy due to subsampling Probabilistic predicate discovery

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Conclusion Markov logic networks: a powerful combination

of first-order logic and probability Richardson & Domingos (2004) did not learn

MLN structure We develop an algorithm that automatically learns

both first-order clauses and their weights We develop speedup techniques to make our

algorithm fast enough to be practical We show experimentally that our algorithm

outperforms Richardson & Domingos Pure ILP Purely KB approaches Purely probabilistic approaches

(For software, email: koks@cs.washington.edu)