Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
复制标题
DOI：
--
复制
发表时间：
2020-02
期刊：
影响因子：
--
通讯作者：
Tal Friedman;Guy Van den Broeck
中科院分区：
文献类型：
--
作者：
Tal Friedman;Guy Van den Broeck
我们提出了概率数据库和关系嵌入模型的统一技术，目标是对不完整和不确定的数据执行复杂查询。我们形式化了一个概率数据库模型，所有的查询都是根据该模型进行的。这使我们能够利用来自概率数据库的丰富的理论和算法文献来解决问题。虽然这种形式化可以与任何关系嵌入模型一起使用，但由于缺乏定义良好的联合概率分布，使得简单的查询问题变得非常困难。考虑到这一点，我们引入了一种关系嵌入模型，该模型被设计为一个易于处理的概率数据库，通过利用概率框架中的典型嵌入假设。我们使用一个原则性的、高效的推理算法，从它的定义出发，实证地证明了TOS是一种有效和通用的查询任务模型。
We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with respect to which all queries are done. This allows us to leverage the rich literature of theory and algorithms from probabilistic databases for solving problems. While this formalization can be used with any relational embedding model, the lack of a well-defined joint probability distribution causes simple query problems to become provably hard. With this in mind, we introduce \TO, a relational embedding model designed to be a tractable probabilistic database, by exploiting typical embedding assumptions within the probabilistic framework. Using a principled, efficient inference algorithm that can be derived from its definition, we empirically demonstrate that \TOs is an effective and general model for these querying tasks.