Foundations of logic programming
Foundations of logic programming
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An analysis of first-order logics of probability
Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Bayesian Logic Programs
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Learning statistical models from relational data
Learning statistical models from relational data
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Iterative record linkage for cleaning and integration
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Effective Bayesian inference for stochastic programs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Understanding tuberculosis epidemiology using structured statistical models
Artificial Intelligence in Medicine
Principles of dataspace systems
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Reasoning with recursive loops under the PLP framework
ACM Transactions on Computational Logic (TOCL)
The Complexity of Translating BLPs to RMMs
Inductive Logic Programming
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Learning first-order probabilistic models with combining rules
Annals of Mathematics and Artificial Intelligence
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
A relational hierarchical model for decision-theoretic assistance
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Exploiting causal independence in Markov logic networks: combining undirected and directed models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Probabilistic logics in expert systems: approaches, implementations, and applications
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into a logic programming framework. We show how to represent varying degrees of complexity in the semantics including attribute uncertainty, structural uncertainty and identity uncertainty. Our approach is similar in spirit to the work in Bayesian Logic Programs (BLPs), and Logical Bayesian Networks (LBNs). However, surprisingly, there are still some important differences in the resulting formalism; for example, we introduce a general notion of aggregates based on the PRM approaches. One of our contributions is that we show how to support richer forms of structural uncertainty in a probabilistic logical language than have been previously described. Our goal in this work is to present a unifying framework that supports all of the types of relational uncertainty yet is based on logic programming formalisms. We also believe that it facilitates understanding the relationship between the frame-based approaches and alternate logic programming approaches, and allows greater transfer of ideas between them.