SlimShot: In-Database Probabilistic Inference for Knowledge Bases
SlimShot: In-Database Probabilistic Inference for Knowledge Bases
复制标题
SlimShot：知识库的数据库内概率推理
DOI：
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发表时间：
2016
影响因子：
2.5
通讯作者：
Dan Suciu
中科院分区：
文献类型：
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作者：
Eric Gribkoff;Dan Suciu
通过爬网或其他文档来创建越来越大的知识库，并使用机器学习技术提取事实和关系。为了管理数据中的不确定性，这些KB依赖于基于马尔可夫逻辑网络（MLN）的概率引擎，概率推断仍然是一个重大挑战。当今的最先进的系统使用MCMC的变体，这些变体没有理论上的错误保证，并且正如我们所显示的，在实践中的性能不佳。
在本文中，我们描述了Slimshot（可扩展的推理和蒙特卡洛采样混合优化技术），这是一种用于知识库的概率推理引擎。 Slimshot将MLN转换为独立于元组的概率数据库，然后使用简单的基于蒙特卡洛的推理，并具有三个关键增强功能：（1）它结合了采样与安全查询评估，（2）通过共同计算有条件的概率来计算条件性概率分子和分母，以及（3）根据样本基数调整提案分布。结合起来，这三种技术使我们能够提供正式的错误保证，并且从经验上说，Slimshot的表现要优于当今知识库中使用的最先进的概率推理引擎。
Increasingly large Knowledge Bases are being created, by crawling the Web or other corpora of documents, and by extracting facts and relations using machine learning techniques. To manage the uncertainty in the data, these KBs rely on probabilistic engines based on Markov Logic Networks (MLN), for which probabilistic inference remains a major challenge. Today's state of the art systems use variants of MCMC, which have no theoretical error guarantees, and, as we show, suffer from poor performance in practice.
In this paper we describe SlimShot (Scalable Lifted Inference and Monte Carlo Sampling Hybrid Optimization Technique), a probabilistic inference engine for knowledge bases. SlimShot converts the MLN to a tuple-independent probabilistic database, then uses a simple Monte Carlo-based inference, with three key enhancements: (1) it combines sampling with safe query evaluation, (2) it estimates a conditional probability by jointly computing the numerator and denominator, and (3) it adjusts the proposal distribution based on the sample cardinality. In combination, these three techniques allow us to give formal error guarantees, and we demonstrate empirically that SlimShot outperforms to-day's state of the art probabilistic inference engines used in knowledge bases.
影响因子：
14.4
作者：
Hoffart, Johannes;Suchanek, Fabian M.;Weikum, Gerhard
通讯作者：
Weikum, Gerhard