Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

Tal Friedman, Guy Broeck
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1268-1277, 2020.

Abstract

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 TractOR, 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 TractOR is an effective and general model for these querying tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-friedman20a, title = {Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings}, author = {Friedman, Tal and Van den Broeck, Guy}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {1268--1277}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/friedman20a/friedman20a.pdf}, url = {https://proceedings.mlr.press/v124/friedman20a.html}, abstract = {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 TractOR, 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 TractOR is an effective and general model for these querying tasks.} }
Endnote
%0 Conference Paper %T Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings %A Tal Friedman %A Guy Broeck %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-friedman20a %I PMLR %P 1268--1277 %U https://proceedings.mlr.press/v124/friedman20a.html %V 124 %X 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 TractOR, 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 TractOR is an effective and general model for these querying tasks.
APA
Friedman, T. & Broeck, G.. (2020). Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:1268-1277 Available from https://proceedings.mlr.press/v124/friedman20a.html.

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