Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
This work addresses the challenge of drawing valid conclusions from ubiquitous statistical information, but it appears incremental as it applies an existing method (knowledge graph embeddings) to a new domain (Statistical EL).
The paper tackled the problem of efficiently approximating probabilistic inference in Statistical EL using knowledge graph embeddings, achieving runtime and soundness guarantees with empirical evaluation of approximation quality.
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.