1. Statistical Relational AI
590AI/DMSL Seminar
Autumn 2003

2. Overview The AI view The data mining view The statistical view
Applications
Relational extensions of statistical models
Statistical extensions of first-order logic
Major problem types
Crosscutting issues
Plan

3. Statistical Relational AI
The AI View
Statistical Relational AI
Probability
First-Order Logic
Propositional Logic

4. 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

5. 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

6. 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, IIW-03)
Etc.

7. Relational Extensions of Statistical Models
Probabilistic relational models (Friedman et al, IJCAI-99)
Relational Markov networks (Taskar et al, UAI-02)
Relational Markov models (Anderson et al, KDD-02)
Relational dependency networks (Neville & Jensen, MRDM-03)
Stochastic graph grammars (Oates et al, SRL-03)
Etc.

8. Statistical Extensions of First-Order Logic
Knowledge-based model construction (Wellman et al, 1992)
Stochastic logic programs (Muggleton, 1996)
PRISM (Sato & Kameya, 1997)
Bayesian logic programs (Kersting, 2000)
CLP(BN) (Costa et al, 2003)
Etc.

9. Major Problem Types Collective classification Link discovery
Link-based search
Link-based clustering
Co-clustering
Learning across distributions
Object identification
Etc.

10. Cross-Cutting Issues Propositionalization and aggregation
Efficient inference and learning
Incorporation of knowledge
Integration with databases
Generative vs. discriminative learning
Time-changing data
Structuring the field
Etc.

11. Plan Next week: Overview of background by Matt Richardson
Review uncertainty, first-order logic (Good source: Russell & Norvig, AIMA 2nd ed.)
Following weeks: Paper presentations
Volunteer to present a paper
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