Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Machine Learning - special issue on inductive logic programming
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Relational Dependency Networks
The Journal of Machine Learning Research
Statistical predicate invention
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Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Learning to assign degrees of belief in relational domains
Machine Learning
Structured machine learning: the next ten years
Machine Learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Learning Dynamic Bayesian Networks Structure Based on Bayesian Optimization Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Comparison between Two Statistical Relational Models
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Deep transfer via second-order Markov logic
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Discovering and representing systematic code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Artificial General Intelligence through Large-Scale, Multimodal Bayesian Learning
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Structure Learning of Markov Logic Networks through Iterated Local Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Integrating multiple learning components through Markov logic
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Change of representation for statistical relational learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Merging stories with shallow semantics
KRAQ '06 Proceedings of the Workshop KRAQ'06 on Knowledge and Reasoning for Language Processing
Transfer learning from minimal target data by mapping across relational domains
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Probabilistic inductive logic programming
Detecting duplicate biological entities using Shortest Path Edit Distance
International Journal of Data Mining and Bioinformatics
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Online structure learning for Markov logic networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Probabilistic rule learning in nonmonotonic domains
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
Learning models of relational stochastic processes
ECML'05 Proceedings of the 16th European conference on Machine Learning
Reference classes and relational learning
International Journal of Approximate Reasoning
Prolog performance on larger datasets
PADL'07 Proceedings of the 9th international conference on Practical Aspects of Declarative Languages
Probabilistic databases with MarkoViews
Proceedings of the VLDB Endowment
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ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Conceptual clustering of multi-relational data
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
Monte Carlo MCMC: efficient inference by approximate sampling
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Combining relational learning with SMT solvers using CEGAR
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
Type Extension Trees for feature construction and learning in relational domains
Artificial Intelligence
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Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive logic programming (ILP) and feature induction in Markov networks. The algorithm performs a beam or shortest-first search of the space of clauses, guided by a weighted pseudo-likelihood measure. This requires computing the optimal weights for each candidate structure, but we show how this can be done efficiently. The algorithm can be used to learn an MLN from scratch, or to refine an existing knowledge base. We have applied it in two real-world domains, and found that it outperforms using off-the-shelf ILP systems to learn the MLN structure, as well as pure ILP, purely probabilistic and purely knowledge-based approaches.