Non-parametric learning of embeddings for relational data using Gaifman locality theorem

DOI10.1007/978-3-030-97454-1_7zbMath1524.68270OpenAlexW4213414043MaRDI QIDQ6166549

Gautam Kunapuli, Sriraam Natarajan, Devendra Singh Dhami, Siwen Yan

Publication date: 3 August 2023

Published in: Inductive Logic Programming (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-97454-1_7

zbMATH Keywords

embeddingsstatistical relational learningGaifman locality theoremrelational density estimation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Gradient-based boosting for statistical relational learning: the relational dependency network case
• Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases
• Top-down induction of first-order logical decision trees
• Relational retrieval using a combination of path-constrained random walks
• Relational restricted Boltzmann machines: a probabilistic logic learning approach
• Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
• Knowledge Graph Completion via Complex Tensor Factorization

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