On Constrained Open-World Probabilistic Databases
On Constrained Open-World Probabilistic Databases
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DOI：
10.24963/ijcai.2019/793
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Tal Friedman;Guy Van den Broeck
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文献类型：
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作者：
Tal Friedman;Guy Van den Broeck
可用数据量的增加导致对表示大规模概率知识库的需求不断增加。一种方法是使用概率数据库，这是一种具有强大假设的模型，可以有效地回答许多有趣的查询。最近关于开放世界概率数据库的工作通过放弃数据中不存在的任何信息必定是错误的假设来增强这些概率数据库的语义。虽然直观，但这些语义不够精确，无法为查询提供合理的答案。我们建议通过使用约束来限制这个开放世界来克服这些问题。我们为一类查询提供算法，并为另一类查询建立基本硬度结果。最后，我们为一大类查询提出了一种高效且严格的近似。
Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries.