论文标题
Gaifman模型的非参数学习
Non-Parametric Learning of Gaifman Models
论文作者
论文摘要
我们考虑了Gaifman模型的结构学习问题，并学习可用于从知识库得出特征表示的关系特征。这些关系特征是一阶规则，然后在Gaifman模型的本地社区中进行部分接地并计数以获取特征表示。我们提出了一种通过使用关系树距离来学习Gaifman模型的这些关系特征的方法。我们对实际数据集的经验评估表明，我们的方法优于经典规则学习。
We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations. We propose a method for learning these relational features for a Gaifman model by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over classical rule-learning.
