cpsc 422, lecture 34slide 1 intelligent systems (ai-2) computer science cpsc422, lecture 34 apr, 10,...

CPSC 422, Lecture 34 Slide 1

Intelligent Systems (AI-2)

Computer Science cpsc422, Lecture 34

Apr, 10, 2015Slide source: from Pedro Domingos UW & Markov Logic: An Interface Layer for Artificial Intelligence Pedro Domingos and Daniel Lowd University of Washington, Seattle

CPSC 422, Lecture 34 2

Lecture Overview• Finish Inference in MLN

• Probability of a formula, Conditional Probability

• Markov Logic: applications• (422) Highlights from IUI conference• Watson…. • TA evaluation / Teaching Evaluations• Final Exam (office hours, samples)

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 C of constants, It defines a Markov network with

One binary node for each grounding of each predicate in the MLN

One feature/factor for each grounding of each formula F in the MLN, with the corresponding weight w


Grounding: substituting vars with constants

MLN features

)()(),(,

)()(

ySmokesxSmokesyxFriendsyx

xCancerxSmokesx

1.1

5.1

Cancer(A)

Smokes(A)Friends(A,A)

Friends(B,A)

Smokes(B)

Friends(A,B)

Cancer(B)

Friends(B,B)

Two constants: Anna (A) and Bob (B)


Computing Cond. Probabilities

Let’s look at the simplest case

P(ground literal | conjuction of ground literals, ML,C)


P(Cancer(B)| Smokes(A), Friends(A, B), Friends(B, A) )

To answer this query do you need to create (ground) the whole network?

Cancer(A)

Smokes(A)Friends(A,A)

Friends(B,A)

Smokes(B)

Friends(A,B)

Cancer(B)

Friends(B,B)

)()(),(,

)()(


xCancerxSmokesx

1.1

5.1

Computing Cond. ProbabilitiesLet’s look at the simplest case

P(ground literal | conjuction of ground literals, ML,C)



You do not need to create (ground) the part of the Markov Network from which the query is independent given the evidence

Computing Cond. Probabilities



You can then perform Gibbs Sampling in

this Sub Network

The sub network is determined by the formulas

(the logical structure of the problem)

)()(),(,

)()(


xCancerxSmokesx

1.1

5.1

9

Entity Resolution

CPSC 422, Lecture 34

• Determining which observations correspond to the same real-world objects

• (e.g., database records, noun phrases, video regions, etc)

• Crucial importance in many areas (e.g., data cleaning, NLP, Vision)

10

Entity Resolution: ExampleAUTHOR: H. POON & P. DOMINGOSTITLE: UNSUPERVISED SEMANTIC PARSINGVENUE: EMNLP-09

AUTHOR: Hoifung Poon and Pedro DomingsTITLE: Unsupervised semantic parsingVENUE: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

AUTHOR: Poon, Hoifung and Domings, PedroTITLE: Unsupervised ontology induction from textVENUE: Proceedings of the Forty-Eighth Annual Meeting of the Association for Computational Linguistics

AUTHOR: H. Poon, P. DomingsTITLE: Unsupervised ontology inductionVENUE: ACL-10

SAME?

SAME?


11

Problem: Given citation database, find duplicate recordsEach citation has author, title, and venue fieldsWe have 10 relations

Author(bib,author)Title(bib,title)Venue(bib,venue)

HasWord(author, word)HasWord(title, word)HasWord(venue, word)

SameAuthor (author, author)SameTitle(title, title)SameVenue(venue, venue)

SameBib(bib, bib)

Entity Resolution (relations)


indicate which words are present in each field;

represent field equality;

represents citation equality;

relate citations to their fields

12

Predict citation equality based on words in the fields

Title(b1, t1) Title(b2, t2) ∧ ∧HasWord(t1,+word) HasWord(t2,+word) ∧ ⇒SameBib(b1, b2)

(NOTE: +word is a shortcut notation, you actually have a rule for each word e.g., Title(b1, t1) Title(b2, t2) ∧ ∧HasWord(t1,”bayesian”) ∧HasWord(t2,”bayesian” ) SameBib(b1, b2) )⇒

Same 1000s of rules for author

Same 1000s of rules for venue

Entity Resolution (formulas)


13

Transitive closureSameBib(b1,b2) SameBib(b2,b3) SameBib(b1,b3)∧ ⇒

SameAuthor(a1,a2) SameAuthor(a2,a3) SameAuthor(a1,a3)∧ ⇒Same rule for titleSame rule for venue

Entity Resolution (formulas)


Link fields equivalence to citation equivalence – e.g., if two citations are the same, their authors should be the same Author(b1, a1) Author(b2, a2) SameBib(b1, b2) ∧ ∧ ⇒SameAuthor(a1, a2)…and that citations with the same author are more likely to be the sameAuthor(b1, a1) Author(b2, a2) SameAuthor(a1, a2) ∧ ∧ SameBib(b1, b2)⇒

Same rules for titleSame rules for venue

Benefits of MLN model


Standard non-MLN approach: build a classifier that given two citations tells you if they are the same or not, and then apply transitive closure

New MLN approach: • performs collective entity resolution, where

resolving one pair of entities helps to resolve pairs of related entities

e.g., inferring that a pair of citations are equivalent can provide evidence that the names AAAI-06 and 21st Natl. Conf. on AI refer to the same venue, even though they are superficially very different. This equivalence can then aid in resolving other entities.

15

Other MLN applications


• Information Extraction

• Co-reference Resolution (see lecture 1!)

• Robot Mapping (infer the map of an indoor environment from laser range data)

• Link-based Clustering (uses relationships among the objects in determining similarity)

• Ontologies extraction from Text

• …..

422 big picture

Query

Planning

Deterministic Stochastic

• Value Iteration• Approx.

Inference

• Full Resolution

• SAT

LogicsBelief Nets

Markov Decision Processes and

Partially Observable MDP

Markov Chains and HMMs

First Order LogicsDescription Logics/

OntologiesTemporal rep.

Applications of AI

More sophisticated reasoning

Undirected Graphical Models

Conditional Random Fields

Reinforcement Learning

Representation

ReasoningTechnique

Prob relational models

Prob CFGMarkov Logics

Hybrid


422 big picture

Query

Planning

Deterministic Stochastic

• Value Iteration• Approx.

Inference

• Full Resolution

• SAT

LogicsBelief Nets

Markov Decision Processes and

Partially Observable MDP

Markov Chains and HMMs

First Order Logics

OntologiesTemporal rep.

Applications of AI

Approx. : Gibbs

Undirected Graphical Models

Conditional Random Fields

Reinforcement Learning

Representation

ReasoningTechnique

Prob CFGProb Relational

ModelsMarkov Logics

Hybrid: Det +Sto

Forward, Viterbi….Approx. : Particle

Filtering



AI and HCI meet

Keynote Speaker:Prof. Dan Weld, University of WashingtonIntelligent Control of CrowdsourcingCrowd-sourcing labor markets (e.g., Amazon Mechanical Turk) are booming, …… use of partially-observable Markov decision Processes (POMDPs) to control voting on binary-choice questions and iterative improvement workflows.

Some papers from IUI

Unsupervised Modeling of Users' Interests from their Facebook Profiles and Activities (Page 191)Preeti Bhargava (University of Maryland)Oliver Brdiczka (Vectra Networks, Inc.)Michael Roberts (Palo Alto Research Center)

named entity recognition, document categorization, sentiment analysis, semantic relatedness and social tagging

Semantic Textual Similarity (STS) system [13] for computing the SR scores. STS is based on LSA along with WordNet knowledge and is trained on LDC Gigawords and Stanford Webbase corpora


Some papers from IUI

BayesHeart: A Probabilistic Approach for Robust, Low-Latency Heart Rate Monitoring on Camera Phones Xiangmin Fan (University of Pittsburgh)Jingtao Wang (University of Pittsburgh)

BayesHeart is based on an adaptive hidden Markov model, requires minimal training data and is user-independent.

Two models, one with 2 states and one with 4 states, work in combination….


Watson : analyzes natural language questions and content well enough and fast enough to compete and win against champion players at Jeopardy!

CPSC 422, Lecture 34 Slide 23Source:IBM

“This Drug has been shown to relieve the symptoms of ADD with relatively few side effects."

• 1000s of algorithms and KBs,

• 3 secs

https://researcher.ibm.com/researcher/view_page.php?id=2158

AI techniques in 422 / Watson• Parsing (PCFGs)• Shallow parsing (NP segmentation with CRFs)• Entity and relation Detection (NER with CRFs)• Logical Form Generation and Matching• Logical Temporal and Spatial Reasoning• Leveraging many databases, taxonomies,

and ontologies• Confidence…. Probabilities (Bnets to rank)• Strategy for playing Jeopardy…statistical

models of players and games, game-theoretic analyses … .. and application of reinforcement-learningCPSC 422, Lecture 34 Slide 24

From silly project to $1 billion investment

2005-6 “IT’S a silly project to work on, it’s too gimmicky, it’s not a real computer-science test, and we probably can’t do it anyway.” These were reportedly the first reactions of the team of IBM researchers challenged to build a computer system capable of winning “Jeopardy!


On January 9th 2014, with much fanfare, the computing giant announced plans to invest $1 billion in a new division, IBM Watson Group. By the end of the year, the division expects to have a staff of 2,000 plus an army of external app developers …..Mike Rhodin, who will run the new division, calls it “one of the most significant innovations in the history of our company.” Ginni Rometty, IBM’s boss since early 2012, has reportedly predicted that it will be a $10 billion a year business within a decade.

………after 8-9 years…

And interactive, collaborative question-answering / problem solving

More complex questions in the future…

Or something like: “Should Europe reduce its energy dependency from Russia and what would it take?”


AI applications…….

• DeepQA• Robotics• Search Engines• Games• Tutoring Systems• Medicine / Finance / …..• …….


• Most companies are investing in AI and/or developing/adopting AI technologies


Lecture Overview

• Finish Inference in MLN• Probability of a formula, Conditional

Probability



TA evaluation

Issam Laradji [email protected]

Also if you have not done it yet, fill out the teaching evaluationshttps://eval.olt.ubc.ca/science. login to the site using your CWL


• Learning Goals (look at the end of the slides for each lecture)

• Revise all the clicker questions, practice exercises, assignments and midterm

• Will post more practice material today• Office Hours – usual ones Me Mon 1-2, Issam

Fri 11-12• – if high demand we will add a few more.

• Can bring letter sized sheet of paper with anything written on it (double sided)

Final Exam, Sat, Apr 18, we will start at 12:00PM Location:

DMP 110How to prepare….

32

Example: Coreference Resolution

Mentions of Obama are often headed by "Obama"Mentions of Obama are often headed by "President"Appositions usually refer to the same entity

Barack Obama, the 44th President of the United States, is the first African American to hold the office. ……


33

Example: Coreference Resolution

1.5

0.8

100

Head(A,“Obama”)

MentionOf(A,Obama)

Head(A,“President”)

MentionOf(B,Obama)

Apposition(A,B)

Head(B,“Obama”)

Head(B,“President”)

Two mention constants: A and B

Apposition(B,A)

MentionOf ( ,Obama) Head( ,"Obama")

MentionOf ( ,Obama) Head( ,"President")

, , Apposition( , ) MentionOf ( , ) MentionOf ( , )

x x x

x x x

x y c x y x c y c

In general, they represent feature templates for Markov NetworksCPSC 422, Lecture 34

34

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.

Entity Resolution


35

UNSUPERVISED SEMANTIC PARSING. H. POON & P. DOMINGOS. EMNLP-2009.

Unsupervised Semantic Parsing, Hoifung Poon and Pedro Domingos. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Singapore: ACL.

Information Extraction


36

UNSUPERVISED SEMANTIC PARSING. H. POON & P. DOMINGOS. EMNLP-2009.

Unsupervised Semantic Parsing, Hoifung Poon and Pedro Domingos. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Singapore: ACL.

Information ExtractionAuthor Title Venue

SAME?


37


• Problem: Extract database from text orsemi-structured sources

• Example: Extract database of publications from citation list(s) (the “CiteSeer problem”)

• Two steps:– Segmentation:

Use HMM to assign tokens to fields– Entity resolution:

Use logistic regression and transitivityCPSC 422, Lecture 34

38

Token(token, position, citation)InField(position, field!, citation)SameField(field, citation, citation)SameCit(citation, citation)

Token(+t,i,c) => InField(i,+f,c)InField(i,+f,c) ^ InField(i+1,+f,c)

Token(+t,i,c) ^ InField(i,+f,c) ^ Token(+t,i’,c’) ^ InField(i’,+f,c’) => SameField(+f,c,c’)SameField(+f,c,c’) <=> SameCit(c,c’)SameField(f,c,c’) ^ SameField(f,c’,c”) => SameField(f,c,c”)SameCit(c,c’) ^ SameCit(c’,c”) => SameCit(c,c”)



39

Token(token, position, citation)InField(position, field!, citation)SameField(field, citation, citation)SameCit(citation, citation)

Token(+t,i,c) => InField(i,+f,c)InField(i,+f,c) ^ !Token(“.”,i,c) ^ InField(i+1,+f,c)

Token(+t,i,c) ^ InField(i,+f,c) ^ Token(+t,i’,c’) ^ InField(i’,+f,c’) => SameField(+f,c,c’)SameField(+f,c,c’) <=> SameCit(c,c’)SameField(f,c,c’) ^ SameField(f,c’,c”) => SameField(f,c,c”)SameCit(c,c’) ^ SameCit(c’,c”) => SameCit(c,c”)

More: H. Poon & P. Domingos, “Joint Inference in InformationExtraction”, in Proc. AAAI-2007.



Inference in MLN• MLN acts as a template for a Markov

Network• We can always answer prob. queries

using standard Markov network inference methods on the instantiated network

• However, due to the size and complexity of the resulting network, this is often infeasible.

• Instead, we combine probabilistic methods with ideas from logical inference, including satisfiability and resolution.

• This leads to efficient methods that take full advantage of the logical structure.


MAP Inference

• Reduces to finding the pw that maximizes the sum of weights of satisfied clauses

• Use weighted SAT solver(e.g., MaxWalkSAT [Kautz et al., 1997])


i

iipw

pwnw )(maxarg

Find most likely state of world

)(maxarg pwPpw

Probabilistic problem solved by logical inference method

Computing Probabilities

P(Formula,ML,C) = ?• Brute force: Sum probs. of possible

worlds where formula holds

• MCMC: Sample worlds, check formula holds


F

CL

PW

M ,

FPWpw CLCL MpwPMFP ),(),( ,,

||

||),( , S

SMFP

S

S

FCL

F

cpsc 422, lecture 34slide 1 intelligent systems (ai-2) computer science cpsc422, lecture 34 apr, 10,...

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