论文标题
向量空间的符号查询：概率数据库符合关系嵌入
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
论文作者
论文摘要
我们提出了概率数据库和关系嵌入模型的统一技术，目的是在不完整和不确定的数据上执行复杂的查询。我们对所有查询进行的概率数据库模型进行了形式化。这使我们能够利用概率数据库中的理论和算法丰富的文献来解决问题。尽管这种形式化可以与任何关系嵌入模型一起使用，但缺乏明确的关节概率分布会导致简单的查询问题变得很难。考虑到这一点，我们通过利用概率框架内的典型嵌入假设来介绍一个关系嵌入模型，该模型旨在成为一种可处理的概率数据库。使用可以从其定义中得出的原则，有效的推理算法，我们从经验上证明\ tos是这些查询任务的有效且通用的模型。
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.
