Concretely, using backward walks, one can compute random walk probabilities in a bi-directional fashion; this means that for paths of length 2M, the time ...
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- ...
... PR-based methods determine the missing relation through training Random Walk (RW) joint probability of selected paths, and also the corresponding relation ...
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Learning Relational Features with Backward Random Walks. - DBLP
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Aug 6, 2021 · Bibliographic details on Learning Relational Features with Backward Random Walks.
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 ...
Abstract: The path ranking algorithm (PRA) has been recently proposed to address relational classification and retrieval tasks at large scale.
Each element of the similarity matrix contains a measure of similarity between two of the data points. Similarity matrices are strongly related to their ...
Learning Relational Features with Backward Random Walks. Ni Lao | Einat Minkov | William Cohen |. Paper Details: Month: July Year: 2015
Table of Contents · Abstract 2012; Gardner et al., 2013; Gardner et al., 2014; · 1 Introduction · 2 Related Work · 3 Background. 3.1 Path Ranking Algorithm; 3.2 PRA ...
We describe and test faster algorithms for searching for these features. A key contribution is to leverage backward random walks to efficiently discover these ...