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This research proposes using knowledge graph embeddings to efficiently approximate probabilistic inference within Statistical EL, addressing the computational difficulty of drawing valid conclusions from statistical information. The authors provide formal proofs for runtime and soundness guarantees alongside empirical evaluations of approximation quality, offering a theoretically grounded approach for handling ubiquitous statistical data.
Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
arXiv:2407.11821v2 Announce Type: replace 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2407.11821 [cs.AI]
(or arXiv:2407.11821v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2407.11821 Focus to learn more arXiv-issued DOI via DataCite
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