Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
arXiv:1206.3282
Abstract
In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta algorithm that instantiates small parts of a large and complex Markov Network and then solves these using a conventional MAP method. We evaluate CPI on two tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in two different MAP inference methods: the current method of choice for MAP inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We observe that when used with CPI both methods are significantly faster than when used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains the exactness of Integer Linear Programming.
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)
References in corpus (1)
Cited by in corpus (13)
- Probabilistic Similarity Logic
- Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
- Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
- Location-Based Reasoning about Complex Multi-Agent Behavior
- Markov Chains on Orbits of Permutation Groups
- Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
- Extended Report: Fine-grained Recognition of Abnormal Behaviors for Early Detection of Mild Cognitive Impairment
- Beyond Markov Logic: Efficient Mining of Prediction Rules in Large Graphs
- XOR-Sampling for Network Design with Correlated Stochastic Events
- Ontology Matching with Knowledge Rules
- Inference by Minimizing Size, Divergence, or their Sum
- Lifted Graphical Models: A Survey
- Scaling Inference for Markov Logic with a Task-Decomposition Approach