Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
This work addresses the challenge of querying uncertain data for applications in databases and AI, representing an incremental advancement by integrating existing techniques into a novel framework.
The paper tackles the problem of performing complex queries on incomplete and uncertain data by unifying probabilistic databases and relational embedding models, resulting in the introduction of TO, a tractable model that enables efficient inference and demonstrates effectiveness in querying tasks.
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.