Knowledge Hypergraph Embedding Meets Relational Algebra

• URL: http://arxiv.org/abs/2102.09557v1
• Date: Thu, 18 Feb 2021 18:57:44 GMT
• Title: Knowledge Hypergraph Embedding Meets Relational Algebra
• Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole
• Abstract summary: We propose a simple embedding-based model called ReAlE that performs link prediction in knowledge hypergraphs. We show theoretically that ReAlE is fully expressive and provide proofs and empirical evidence that it can represent a large subset of the primitive relational algebra operations.
• Score: 13.945694569456665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying the relations in the graph. We propose a simple embedding-based model called ReAlE that performs link prediction in knowledge hypergraphs (generalized knowledge graphs) and can represent high-level abstractions in terms of relational algebra operations. We show theoretically that ReAlE is fully expressive and provide proofs and empirical evidence that it can represent a large subset of the primitive relational algebra operations, namely renaming, projection, set union, selection, and set difference. We also verify experimentally that ReAlE outperforms state-of-the-art models in knowledge hypergraph completion, and in representing each of these primitive relational algebra operations. For the latter experiment, we generate a synthetic knowledge hypergraph, for which we design an algorithm based on the Erdos-R'enyi model for generating random graphs.

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