Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption. We prove generalization error bounds for this setting. Even though the missing completely at random setting may seem naive, it is actually how knowledge graph embedding methods are typically benchmarked in the literature. Our results provide, to certain extent, an explanation for why knowledge graph embedding methods work (as much as classical learning theory results provide explanations for classical learning from i.i.d. data).
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