Scalable Rule Learning in Probabilistic Knowledge Bases
Scalable Rule Learning in Probabilistic Knowledge Bases
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DOI：
10.24432/c5mw26
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发表时间：
2019-05
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影响因子：
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通讯作者：
Arcchit Jain;Tal Friedman;Ondřej Kuželka;Guy Van den Broeck;L. D. Raedt
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文献类型：
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作者：
Arcchit Jain;Tal Friedman;Ondřej Kuželka;Guy Van den Broeck;L. D. Raedt
知识库 (KB) 变得越来越大、越来越稀疏和概率性。这些知识库通常用于执行查询推理和规则挖掘。但其有效性取决于其完整性。有效利用不完整的知识库仍然是一个主要挑战，因为当前的知识库补全技术要么没有考虑与每个知识库元组相关的固有不确定性，要么没有扩展到大型知识库。概率规则学习不仅考虑每个知识库元组的概率，而且以可解释的方式解决知识库补全问题。对于任何给定的概率知识库，它从其关系中学习概率一阶规则以识别有趣的模式。但是，当前的概率规则学习技术执行基础以进行概率推理以评估候选规则。它不能很好地扩展到大 KB，因为使用接地进行推理的时间复杂度与 KB 的大小呈指数关系。在本文中，我们提出了 SafeLearner——一种可扩展的概率知识库补全解决方案，它使用提升的概率推理来执行概率规则学习——作为更快的方法而不是基础方法。我们将 SafeLearner 与最先进的概率规则学习器 ProbFOIL+ 以及 NELL（永无止境的语言学习器）和 Yago 的标准概率知识库上的确定性当代 AMIE+ 进行了比较。我们的结果表明，在学习简单规则时，SafeLearner 的扩展能力与 AMIE+ 一样好，并且速度也明显快于 ProbFOIL+。
Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs. Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.