Sya: Enabling Spatial Awareness inside Probabilistic Knowledge Base Construction
Sya: Enabling Spatial Awareness inside Probabilistic Knowledge Base Construction
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DOI：
10.1109/icde48307.2020.00106
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Ibrahim Sabek;M. Mokbel
中科院分区：
文献类型：
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作者：
Ibrahim Sabek;M. Mokbel
本文介绍了Sya；第一个基于马尔科夫逻辑网络（MLN）的空间概率知识库构建系统。Sya将空间关系的意识注入到MLN的基础和推理阶段，这是知识库构建过程的支柱，因此产生了更好的知识库输出。特别是，Sya生成了一个概率模型，该模型捕获了知识库关系之间的逻辑和空间相关性。Sya提供了一种简单的空间高级语言、因子图的空间变化、空间规则查询翻译器和用于推断关系的实际分数的配备了空间的统计推断技术。此外，Sya还提供了一种优化，可确保大规模知识库的可伸缩基础和推理。基于构建两个具有空间性质的真实知识库的实验证据表明，Sya可以比最先进的DeepDive系统平均提高70%的f1分数，同时至少减少20%的执行时间。
This paper presents Sya; the first spatial probabilistic knowledge base construction system, based on Markov Logic Networks (MLN). Sya injects the awareness of spatial relationships inside the MLN grounding and inference phases, which are the pillars of the knowledge base construction process, and hence results in a better knowledge base output. In particular, Sya generates a probabilistic model that captures both logical and spatial correlations among knowledge base relations. Sya provides a simple spatial high-level language, a spatial variation of factor graph, a spatial rules-query translator, and a spatially-equipped statistical inference technique to infer the factual scores of relations. In addition, Sya provides an optimization that ensures scalable grounding and inference for large-scale knowledge bases. Experimental evidence, based on building two real knowledge bases with spatial nature, shows that Sya can achieve 70% higher F1-score on average over the state-of-the-art DeepDive system, while achieving at least 20% reduction in the execution times.