CSE 574 – Artificial Intelligence II

Statistical Relational Learning

Instructor:

Pedro Domingos

Logistics

• Instructor: Pedro DomingosEmail: pedrod@cs.washington.eduOffice: 648 Allen CenterOffice hours: Wednesdays 4:30-5:30

• TA: Stanley KokEmail: koks@cs.washington.eduOffice: 216 Allen CenterOffice hours: Mondays 4:30-5:30

• Web: www.cs.washington.edu/574• Mailing list: cse574

Evaluation

• Seminar (Pass/Fail)

• Project (100% of grade)– Proposals due April 8– Progress report due May 6– Presentation in class– Final report due June 3

Materials

• L. Getoor & B. Taskar (eds.), Statistical Relational Learning, MIT Press (to appear).– Draft chapters– Feedback for authors

• Papers

Topics

• Background

• SRL approaches

• SRL problems and applications

Background

• Statistical learning

• Inductive logic programming

• Sequential and spatial models

SRL Approaches

• Probabilistic relational models

• Stochastic logic programs

• Bayesian logic programs

• Relational Markov networks

• Markov logic networks

• Etc.

SRL Problems and Applications

• Aggregation

• Autocorrelation

• Information extraction and NLP

• Biology and medicine

• Relational reinforcement learning

• Etc.

Today: Introduction

• Motivation– The AI view– The data mining view– The statistical view– The computer science view

• Applications

• Major problem types

• A map of the field

The AI View

Propositional Logic

Probability First-Order Logic

Statistical Relational AI

The Data Mining View

• Most databases contain multiple tables

• Data mining algorithms assume one table

• Manual conversion: slow, costly bottleneck

• Important patterns may be missed

• Solution: Multi-relational data mining

The Statistical View

• Most statistical models assume i.i.d. data(independent and identically distributed)

• A few assume simple regular dependence (e.g., Markov chain)

• This is a huge restriction – Let’s remove it!– Allow dependencies between samples– Allow samples with different distributions

The Computer Science View

• CS faces a complexity bottleneck– Cost of hand-coding– Brittleness

• Machine learning and probability overcome this

• But they mostly apply only to attribute vectors

• Let’s extend them to handle structured objects, class hierarchies, relational databases, etc.

Applications

• Bottom line: Using statistical and relational information gives better results– Web search (Brin & Page, WWW-98)

– Text classification (Chakrabarti et al, SIGMOD-98)

– Marketing (Domingos & Richardson, KDD-01)

– Record linkage (Pasula et al, NIPS-02)

– Gene expression (Segal et al, UAI-03)

– Information extraction (McCallum & Wellner, NIPS-04)

– Etc.

Major Problem Types

• Collective classification• Link discovery• Link-based search• Link-based clustering• Social network analysis• Object identification• Transfer learning• Etc.

A Map of the Field

• There are many approaches(“Alphabet soup”)

• Every year new ones are proposed(and for good reason)

• Key is to understand the major dimensions along which approaches can differ

Major Dimensions

• Probabilistic language

• Logical language

• Type of learning

• Type of inference

• Aggregation

Probabilistic Language

• Bayesian networks

• Markov networks (aka Markov random fields)

• Restrictions of these (e.g., logistic regression)

• Probabilistic context-free grammars

Logical Language

• Prolog / Horn clauses

• Frame systems / Description logics

• Conjunctive database queries

• Full first-order logic

Type of Learning

• Generative vs. discriminative

• Structure vs. parameters

• Knowledge-poor vs. knowledge-rich

Type of Inference

• Marginal/conditional vs. MAP– Marg./cond.: MCMC, belief propagation, etc.– MAP: Graph cuts, weighted satisfiability, etc.

• Full grounding vs. KBMC

Aggregation

• Quantifiers• SQL-like aggregators

(MAX, AVG, SUM, COUNT, MODE, etc.)

• Noisy-OR

• Logistic regression

Examples

• Probabilistic relational models(Friedman et al, IJCAI-99)

• Stochastic logic programs(Muggleton, SRL-00)

• Bayesian logic programs(Kersting & De Raedt, ILP-01)

• Relational Markov networks(Taskar et al, UAI-02)

• Markov logic networks(Richardson & Domingos, SRL-04)