Adaptive Bayesian Logic Programs
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Towards Combining Inductive Logic Programming with Bayesian Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
ACM SIGKDD Explorations Newsletter
PRL: A probabilistic relational language
Machine Learning
Quantitative pharmacophore models with inductive logic programming
Machine Learning
Reasoning with recursive loops under the PLP framework
ACM Transactions on Computational Logic (TOCL)
Induction of Fuzzy and Annotated Logic Programs
Inductive Logic Programming
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
ECML '07 Proceedings of the 18th European conference on Machine Learning
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Belief Logic Programming: Uncertainty Reasoning with Correlation of Evidence
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Integrating Logical Reasoning and Probabilistic Chain Graphs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Belief Logic Programming with Cyclic Dependencies
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Revision of first-order Bayesian classifiers
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Probabilistic information integration and retrieval in the semantic web
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Probabilistic inductive logic programming
Probabilistic inductive logic programming
CLP(BN): constraint logic programming for probabilistic knowledge
Probabilistic inductive logic programming
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Patterns discovery for efficient structured probabilistic inference
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Statistical relational learning: an inductive logic programming perspective
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Statistical relational learning: an inductive logic programming perspective
ECML'05 Proceedings of the 16th European conference on Machine Learning
Combining bayesian networks with higher-order data representations
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Deriving a stationary dynamic bayesian network from a logic program with recursive loops
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Data Mining and Knowledge Discovery
Structured probabilistic inference
International Journal of Approximate Reasoning
Extending and formalizing bayesian networks by strong relevant logic
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. They are a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed in both areas carry over.