Communications of the ACM
The art of Prolog: advanced programming techniques
The art of Prolog: advanced programming techniques
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Simply logical: intelligent reasoning by example
Simply logical: intelligent reasoning by example
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Logical settings for concept-learning
Artificial Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Algorithmic Program DeBugging
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Relational Data Mining
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Structural Learning in Object Oriented Domains
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
ACM SIGKDD Explorations Newsletter
Stochastic attribute-value grammars
Computational Linguistics
Naive Bayesian Classification of Structured Data
Machine Learning
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Towards learning stochastic logic programs from proof-banks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning probabilistic logic models from probabilistic examples
Machine Learning
Structured machine learning: the next ten years
Machine Learning
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Logic-Based Probabilistic Modeling
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Probabilistic Logic Learning - A Tutorial Abstract
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis
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
Query Answering in Belief Logic Programming
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Belief Logic Programming with Cyclic Dependencies
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Learning with kernels and logical representations
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Logic-based representation, reasoning and machine learning for event recognition
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning terminologies in probabilistic description logics
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Towards programming languages for machine learning and data mining
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A survey on question answering technology from an information retrieval perspective
Information Sciences: an International Journal
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
On the implementation of the CLP(BN) language
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
A general MCMC method for Bayesian inference in logic-based probabilistic modeling
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Relational learning for spatial relation extraction from natural language
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
A statistical relational learning approach to identifying evidence based medicine categories
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Using equivalences of worlds for aggregation semantics of relational conditionals
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Solutions for hard and soft constraints using optimized probabilistic satisfiability
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
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Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.