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Markov Logic Networks Matt Richardson and Pedro Domingos (2006), Markov Logic Networks, Machine Learning , 62, 107-136, 2006. CS 786 University of Waterloo Lecture 23: July 19, 2012 Outline Markov Logic Networks Alchemy 2 CS786

  1. Markov Logic Networks Matt Richardson and Pedro Domingos (2006), Markov Logic Networks, Machine Learning , 62, 107-136, 2006. CS 786 University of Waterloo Lecture 23: July 19, 2012 Outline • Markov Logic Networks • Alchemy 2 CS786 Lecture Slides (c) 2012 P. Poupart 1

  2. Markov Logic Networks • Bayesian networks and Markov networks: – Model uncertainty – But propositional representation (e.g., we need one variable per object in the world) • First-order logic: – First-order representation (e.g., quantifiers allow us to reason about several objects simultaneously) – But we can’t deal with uncertainty • Markov logic networks: combine Markov networks and first-order logic 3 CS786 Lecture Slides (c) 2012 P. Poupart Markov Logic • A logical KB is a set of hard constraints on the set of possible worlds • Let’s make them soft constraints : when a world violates a formula, it becomes less probable, not impossible • Give each formula a weight : (higher weight  stronger constraint) P(world)  e  weights of formulas it satisfies 4 CS786 Lecture Slides (c) 2012 P. Poupart 2

  3. Markov Logic: 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 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 5 CS786 Lecture Slides (c) 2012 P. Poupart Example: Friends & Smokers Smoking causes cancer. Friends have similar smoking habits. 6 CS786 Lecture Slides (c) 2012 P. Poupart 3

  4. Example: Friends & Smokers   x Smokes ( x ) Cancer ( x )      x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) 7 CS786 Lecture Slides (c) 2012 P. Poupart Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) 8 CS786 Lecture Slides (c) 2012 P. Poupart 4

  5. Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) 9 CS786 Lecture Slides (c) 2012 P. Poupart Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Smokes(A) Smokes(B) Cancer(A) Cancer(B) 10 CS786 Lecture Slides (c) 2012 P. Poupart 5

  6. Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 11 CS786 Lecture Slides (c) 2012 P. Poupart Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 12 CS786 Lecture Slides (c) 2012 P. Poupart 6

  7. Example: Friends & Smokers   1 . 5 x Smokes ( x ) Cancer ( x )      1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 13 CS786 Lecture Slides (c) 2012 P. Poupart Markov Logic Networks • MLN is template for ground Markov nets • Probability of a world x :   1     P ( x ) exp w n ( x ) i i   Z i Weight of formula i No. of true groundings of formula i in x • Typed variables and constants greatly reduce size of ground Markov net 14 CS786 Lecture Slides (c) 2012 P. Poupart 7

  8. Alchemy • Open Source AI package • http://alchemy.cs.washington.edu • Implementation of Markov logic networks • Problem specified in two files: – File1.mln (Markov logic network) – File2.db (database / data set) • Learn weights and structure of MLN • Inference queries 15 CS786 Lecture Slides (c) 2012 P. Poupart Markov Logic Encoding • File.mln • Two parts: – Declaration • Domain of each variable • Predicates – Formula • Pairs of weights with logical formula 16 CS786 Lecture Slides (c) 2012 P. Poupart 8

  9. Markov Logic Encoding • Example declaration – Domain of each variable • person = {Anna, Bob} – Predicates: • Friends(person,person) • Smokes(person) • Cancer(person) • Example formula – 8 Smokes(x) => Cancer(x) – 5 Friends(x,y) => (Smokes(x)<=>Smokes(y)) NB: by default, formulas are universally quantified in Alchemy 17 CS786 Lecture Slides (c) 2012 P. Poupart Dataset • File.db • List of facts (ground atoms) • Example: – Friends(Anna,Bob) – Smokes(Anna) – Cancer(Bob) 18 CS786 Lecture Slides (c) 2012 P. Poupart 9

  10. Syntax • Logical connective: – ! (not), ^ (and), v (or), => (implies), <=> (iff) • Quantifiers: – forall (  ), exist (  ) – By default unquantified variables are universally quantified in Alchemy • Operator precedence: – ! > ^ > v > => > <=> > forall = exist 19 CS786 Lecture Slides (c) 2012 P. Poupart Syntax • Short hand for predicates – ! operator: indicates that the preceding variable has exactly one true grounding – Ex: HasPosition(x,y!) : for each grounding of x , exactly one grounding of y satisfies HasPosition • Short hand for multiple weights – + operator: indicates that a different weight should be learned for each grounding of the following variable – Ex: outcome(throw,+face): a different weight is learned for each grounding of face 20 CS786 Lecture Slides (c) 2012 P. Poupart 10

  11. Multinomial Distribution Example: Throwing dice Types: throw = { 1, … , 20 } face = { 1, … , 6 } Predicate: Outcome(throw,face) Formulas: Outcome(t,f) ^ f!=f’ => !Outcome(t,f’). Exist f Outcome(t,f). Too cumbersome! 21 CS786 Lecture Slides (c) 2012 P. Poupart Multinomial Distrib.: ! Notation Example: Throwing dice Types: throw = { 1, … , 20 } face = { 1, … , 6 } Predicate: Outcome(throw,face!) Formulas: Semantics: Arguments without “!” determine args with “!”. Only one face possible for each throw. 22 CS786 Lecture Slides (c) 2012 P. Poupart 11

  12. Multinomial Distrib.: + Notation Example: Throwing biased dice Types: throw = { 1, … , 20 } face = { 1, … , 6 } Predicate: Outcome(throw,face!) Formulas: Outcome(t,+f) Semantics: Learn weight for each grounding of args with “+”. 23 CS786 Lecture Slides (c) 2012 P. Poupart Text Classification page = { 1, … , n } word = { … } topic = { … } Topic(page,topic!) HasWord(page,word) Links(page,page) HasWord(p,+w) => Topic(p,+t) Topic(p,t) ^ Links(p,p') => Topic(p',t) 24 CS786 Lecture Slides (c) 2012 P. Poupart 12

  13. Information Retrieval InQuery(word) HasWord(page,word) Relevant(page) Links(page,page) InQuery(+w) ^ HasWord(p,+w) => Relevant(p) Relevant(p) ^ Links(p,p’) => Relevant(p’) Cf. L. Page, S. Brin, R. Motwani & T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” Tech. Rept., Stanford University, 1998. 25 CS486/686 Lecture Slides (c) 2012 P. Poupart Record deduplication Problem: Given database, find duplicate records HasToken(token,field,record) SameField(field,record,record) SameRecord(record,record) HasToken(+t,+f,r) ^ HasToken(+t,+f,r’) => SameField(f,r,r’) SameField(+f,r,r’) => SameRecord(r,r’) SameRecord(r,r’) ^ SameRecord(r’,r”) => SameRecord(r,r”) Cf. A. McCallum & B. Wellner, “Conditional Models of Identity Uncertainty with Application to Noun Coreference,” in Adv. NIPS 17 , 2005. 26 CS486/686 Lecture Slides (c) 2012 P. Poupart 13

  14. Record resolution Can also resolve fields: HasToken(token,field,record) SameField(field,record,record) SameRecord(record,record) HasToken(+t,+f,r) ^ HasToken(+t,+f,r’) => SameField(f,r,r’) SameField(+f,r,r’) <=> SameRecord(r,r’) SameRecord(r,r’) ^ SameRecord(r’,r”) => SameRecord(r,r”) SameField(f,r,r’) ^ SameField(f,r’,r”) => SameField(f,r,r”) More: P. Singla & P. Domingos, “Entity Resolution with Markov Logic”, in Proc. ICDM-2006 . 27 CS486/686 Lecture Slides (c) 2012 P. Poupart Information Extraction • Problem: Extract database from text or semi-structured sources • Example: Extract database of publications from citation list(s) (the “CiteSeer problem”) • Two steps: – Segmentation: Use HMM to assign tokens to fields – Record resolution: Use logistic regression and transitivity 28 CS486/686 Lecture Slides (c) 2012 P. Poupart 14

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