1. Modelling Relational Statistics With Bayes Nets School of Computing Science Simon Fraser University Vancouver, Canada Tianxiang Gao Yuke Zhu

2. 2/12 Class-Level and Instance-Level Queries Classic AI research distinguished two types of probabilistic relational queries. (Halpern 1990, Bacchus 1990). Halpern, “An analysis of first-order logics of probability”, AI Journal 1990. Bacchus, “Representing and reasoning with probabilistic knowledge”, MIT Press 1990. Relational Query Class-level QueryReference Class What is the percentage of flying birds? Birds What is the percentage of friendship pairs where both are women? Pairs of Friends What is the percentage of A grades awarded to highly intelligence students? Student-course pairs where student is registered in course. Instance-Level Query Given that Tweety is a bird, what is the probability that Tweety flies? Given that Sam and Hilary are friends, and given the genders of their other friends, what is the probability that Sam and Hilary are both women? What is the probabiity that Jack is highly intelligent given his grades? Instance-level queries Ground facts Type 2 probabilities Class-level queries Relational Statistics Type 1 probabilities

3. 3/12 Visualizing Class-Level Probability Modelling Relational Statistics With Bayes Nets Percentage of Flying Birds = 90%. Halpern: Probability that a typical or random bird flies is 90%. Contains some free variables. e.g. P(Flies(B)) = ?. Syntactic Distinction Contains no free variables. e.g. P(Flies(tweety)) = ?.

4. 4/12 Applications of Class-Level Modelling 1 st -order rule learning (e.g., “intelligent students take difficult courses”). Strategic Planning (e.g., “increase SAT requirements to decrease student attrition”). Query Optimization (Getoor, Taskar, Koller 2001). Class-level queries support selectivity estimation  optimal evaluation order for SQL query. Getoor, Lise, Taskar, Benjamin, and Koller, Daphne. Selectivity estimation using probabilistic models. ACM SIGMOD Record, 30(2):461–472, 2001.

5. 5/12 No Grounding Semantics for Class- level Queries “Unrolling” a network → model of individual entities.  No classes, cannot ask class-level queries. Modelling Relational Statistics With Bayes Netsa intelligence(S) diff(C) Registered(S,C) Class-level Template with 1st-order Variables intelligence(jack) diff(100) Registered(jack,100) intelligence(jane) diff(200) Registered(jack,200) Registered(jane,100) Registered(jane,200) Instance-level Model w/ domain(S) = {jack,jane} domain(C) = {100,200}

6. 6/12 Previous Work: Probabilistic Queries in Statistical-Relational Learning Class-LevelInstance-Level Statistical-Relational Models (Lise Getoor, Taskar, Koller 2001) Many Model Types: Probabilistic Relational Models, Markov Logic Networks, Bayes Logic Programs, Logical Bayesian Networks, …

7. 7/12 New Unified Approach David Poole, “First-Order Probabilistic Inference”, IJCAI 2003. H. Khosravi, O. Schulte, T. Man, X. Xu, and B. Bina, “Structure learning for Markov logic networks with many descriptive attributes”, in AAAI, 2010. O. Schulte and H. Khosravi. “Learning graphical models for relational data via lattice search”. Machine Learning, 2012. Class-LevelInstance-Level Parametrized Bayes Nets + new class-level semantics Parametrized Bayes Nets + combining rules (Poole 2003) + log-linear model (Khosravi, Schulte et al. 2010, Schulte and Khosravi 2012)

8. 8/12 Random Selection Semantics: Example Apply the random selection semantics for probabilistic 1 st - order logic (Halpern 1990; Bacchus 1990). Halpern, “An analysis of first-order logics of probability”, AI Journal 1990. Bacchus, “Representing and reasoning with probabilistic knowledge”, MIT Press 1990. intelligence(S)diff(C) Registered(S,C) P(intelligence(S) = hi, diff(C) = hi, Registered(S,C) = true) = 20% means: hi true “if we randomly select a student and a course, then the probability is 20% that the student is registered in the course, and that the intelligence of the student and the difficulty of the course are high.”

9. 9/12 Computing Parameter Estimates (I) Use conditional database probabilities as Bayes net parameters. Maximizes the random selection pseudo- likelihood (Schulte 2011). For database probabilities with all true relationships, use SQL or Virtual Join (Yin, Han et al. 2004). Schulte, O. “A tractable pseudo-likelihood function for Bayes nets applied to relational data.” SIAM SDM, 2011. Yin, X., Han. J. et al. “CrossMine: Efficient Classification Across Multiple Database Relations”. Constraint-Based Mining and Inductive Databases, 2004. R1R1 R2R2

10. 10/12 Computing Parameter Estimates (II) How to compute database probabilities for negated relations? e.g., number of U.S. users who are not friends? Materializing complement tables is unscalable. For single false relation, “1-minus trick” (Getoor et al. 2007). General case: New application of the fast Möbius transform (Kennes and Smits 1990). Getoor, Lise, Friedman, Nir, Koller, Daphne, Pfeffer, Avi, and Taskar, Benjamin. Probabilistic relational models, 2007. Kennes, Robert and Smets, Philippe. Computational aspects of the Mobius transformation. In UAI, 1990.

11. 11/12 The Möbius Parametrization Modelling Relational Statistics With Bayes Netsa R1R1 R2R2 Count(*) R1R1 R2R2 R1R1 R2R2 R1R1 R2R2 For two link types R1R1 R2R2 Count(*) R1R1 R2R2 no condition Joint probabilities Möbius Parameters

12. 12/12 Evaluation 1. Fast: parameters in minutes or less. 2. Accurate queries/estimates. 3. Try it yourself in our demo! Modelling Relational Statistics With Bayes Nets