1. CSE 574 – Artificial Intelligence II Statistical Relational Learning Instructor: Pedro Domingos

2. Logistics Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office: 648 Allen Center Office hours: Wednesdays 4:30-5:30 TA: Stanley Kok Email: koks@cs.washington.edu Office: 216 Allen Center Office hours: Mondays 4:30-5:30 Web: www.cs.washington.edu/574 Mailing list: cse574

3. 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

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

5. Topics Background SRL approaches SRL problems and applications

6. Background Statistical learning Inductive logic programming Sequential and spatial models

7. SRL Approaches Probabilistic relational models Stochastic logic programs Bayesian logic programs Relational Markov networks Markov logic networks Etc.

8. SRL Problems and Applications Aggregation Autocorrelation Information extraction and NLP Biology and medicine Relational reinforcement learning Etc.

9. 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

10. The AI View Propositional Logic ProbabilityFirst-Order Logic Statistical Relational AI

11. 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

12. 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

13. 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.

14. 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.

15. Major Problem Types Collective classification Link discovery Link-based search Link-based clustering Social network analysis Object identification Transfer learning Etc.

16. 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

17. Major Dimensions Probabilistic language Logical language Type of learning Type of inference Aggregation

18. Probabilistic Language Bayesian networks Markov networks (aka Markov random fields) Restrictions of these (e.g., logistic regression) Probabilistic context-free grammars

19. Logical Language Prolog / Horn clauses Frame systems / Description logics Conjunctive database queries Full first-order logic

20. Type of Learning Generative vs. discriminative Structure vs. parameters Knowledge-poor vs. knowledge-rich

21. Type of Inference Marginal/conditional vs. MAP –Marg./cond.: MCMC, belief propagation, etc. –MAP: Graph cuts, weighted satisfiability, etc. Full grounding vs. KBMC

22. Aggregation Quantifiers SQL-like aggregators (MAX, AVG, SUM, COUNT, MODE, etc.) Noisy-OR Logistic regression

23. 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)