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We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data.
The expression of a tensor or a polyadic as a sum of products
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Top-down induction of logical decision trees
Hendrik Blockeel and Luc De Raedt · 1997
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Marina Meila and Michael I. Jordan · 1998
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Efficient query evaluation on probabilistic databases
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Herbrand logic
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The dichotomy of conjunctive queries on probabilistic structures
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A three-way model for collective learning on multi-relational data
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Dan Suciu, Dan Olteanu, R. Christopher, and Christoph Koch · 2011
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The dichotomy of probabilistic inference for unions of conjunctive queries
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Ontology-mediated queries for probabilistic databases
Stefan Borgwardt, Ismail Ilkan Ceylan, and Thomas Lukasiewicz · 2017
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Most probable explanations for probabilistic database queries
Ismail Ilkan Ceylan, Stefan Borgwardt, and Thomas Lukasiewicz · 2017
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Query Processing on Probabilistic Data: A Survey
Guy Van den Broeck and Dan Suciu · 2017
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Embedding logical queries on knowledge graphs
William L. Hamilton, Payal Bajaj, Marinka Zitnik, Daniel Jurafsky, and Jure Leskovec · 2018
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Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole · 2018
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A review of relational machine learning for knowledge graphs
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On constrained open-world probabilistic databases
Tal Friedman and Guy Van den Broeck · 2019
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Anytime bottom-up rule learning for knowledge graph completion
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You can teach an old dog new tricks! on training knowledge graph embeddings
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