Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Learning Logical Definitions from Relations
Machine Learning
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Markov network structure discovery using independence tests
Markov network structure discovery using independence tests
Tutorial on statistical relational learning
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Discriminative Structure Learning of Markov Logic Networks
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
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structure Learning of Markov Logic Networks through Iterated Local Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
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
Generative structure learning for Markov logic networks based on graph of predicates
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning the structure of probabilistic logic programs
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
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
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current state-of-the-art algorithm for learning MLN structure follows a top-down paradigm where many potential candidate structures are systematically generated without considering the data and then evaluated using a statistical measure of their fit to the data. Even though this existing algorithm outperforms an impressive array of benchmarks, its greedy search is susceptible to local maxima or plateaus. We present a novel algorithm for learning MLN structure that follows a more bottom-up approach to address this problem. Our algorithm uses a "propositional" Markov network learning method to construct "template" networks that guide the construction of candidate clauses. Our algorithm significantly improves accuracy and learning time over the existing topdown approach in three real-world domains.