An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Principles of Database Systems
Principles of Database Systems
Computational aspects of the Mobius transformation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning statistical models from relational data
Learning statistical models from relational data
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multiplicative latent factor models for description and prediction of social networks
Computational & Mathematical Organization Theory
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Collective graph identification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Markov Logic Networks via Functional Gradient Boosting
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Reference classes and relational learning
International Journal of Approximate Reasoning
Learning graphical models for relational data via lattice search
Machine Learning
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Class-level models capture relational statistics over object attributes and their connecting links, answering questions such as "what is the percentage of friendship pairs where both friends are women?" Class-level relationships are important in themselves, and they support applications like policy making, strategic planning, and query optimization. We represent class statistics using Parametrized Bayes Nets (PBNs), a聽first-order logic extension of Bayes nets. Queries about classes require a new semantics for PBNs, as the standard grounding semantics is only appropriate for answering queries about specific ground facts. We propose a novel random selection semantics for PBNs, which does not make reference to a ground model, and supports class-level queries. The parameters for this semantics can be learned using the recent pseudo-likelihood measure (Schulte in SIAM SDM, pp.聽462---473, 2011) as the objective function. This objective function is maximized by taking the empirical frequencies in the relational data as the parameter settings. We render the computation of these empirical frequencies tractable in the presence of negated relations by the inverse M枚bius transform. Evaluation of our method on four benchmark datasets shows that maximum pseudo-likelihood provides fast and accurate estimates at different sample sizes.