Learning Relational Features with Backward Random Walks
نویسندگان
چکیده
The path ranking algorithm (PRA) has been recently proposed to address relational classification and retrieval tasks at large scale. We describe Cor-PRA, an enhanced system that can model a larger space of relational rules, including longer relational rules and a class of first order rules with constants, while maintaining scalability. We describe and test faster algorithms for searching for these features. A key contribution is to leverage backward random walks to efficiently discover these types of rules. An empirical study is conducted on the tasks of graph-based knowledge base inference, and person named entity extraction from parsed text. Our results show that learning paths with constants improves performance on both tasks, and that modeling longer paths dramatically improves performance for the named entity extraction task.
منابع مشابه
Learning Relational Features with Backward Random Walks
A path learning algorithm (PRA) has been recently proposed that addresses link prediction tasks on heterogenous graphs using learned combinations of labeled paths. Unlike most statistical relational learning methods, this approach scales to large data sets. In this paper, we extend PRA is terms of expressive power, while maintaining its high scalability. Mainly, we propose to compute backward r...
متن کاملTowards First-Order Random Walk Inference
Path Ranking Algorithm (PRA) addresses classification and retrieval tasks using learned combinations of labeled paths through a graph. Unlike most Statistical Relational Learning (SRL) methods, PRA scales to large data sets but uses a limited set of paths in its models—ones that correspond to short first order rules with no constants. We consider extending PRA in two ways—learning paths that co...
متن کاملEfficient Random Walk Inference with Knowledge Bases
Relational learning is a subfield of artificial intelligence, that learns with expressive logical or relational representations. In this thesis, I consider the problem of efficient relational learning. I describe a new relational learning approach based on path-constrained random walks, and demonstrate, with extensive experiments on IR and NLP tasks, how relational learning can be applied at a ...
متن کاملBridging Weighted Rules and Graph Random Walks for Statistical Relational Models
The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship am...
متن کاملRelational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach
We consider the problem of learning Boltzmann machine classifiers from relational data. Our goal is to extend the deep belief framework of RBMs to statistical relational models. This allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL). Specifically, we use lifted random walks to generate...
متن کامل