Approximating Probabilistic Inference in Statistical ℰℒ with Knowledge Graph Embeddings

摘要

In domains where statistical data is collected across hierarchically organized categories, drawing valid conclusions requires reasoning jointly about proportions and the structure of the domain. Statistical ℰℒ (𝒮ℰℒ) formalizes this kind of reasoning, but exact inference is ExpTime-hard and no implementation exists. We show how knowledge graph embeddings can approximate 𝒮ℰℒ inference efficiently. We prove analytical runtime and soundness guarantees, and empirically evaluate the runtime and approximation quality of our approach.