Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings

TL;DR

This paper introduces a method using knowledge graph embeddings to efficiently approximate probabilistic inference in Statistical EL, providing theoretical guarantees and empirical evaluation of performance and accuracy.

Contribution

It presents a novel approach combining knowledge graph embeddings with Statistical EL to enable scalable probabilistic inference with proven guarantees.

Findings

Significant runtime improvements over traditional methods

High approximation accuracy demonstrated empirically

Theoretical proofs of soundness and runtime guarantees

Abstract

Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the example of Statistical EL (SEL), a statistical extension of the lightweight Description Logic EL. We provide proofs for runtime and soundness guarantees, and empirically evaluate the runtime and approximation quality of our approach.