aiqbench analysis
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
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 154 words
Related coverage
Researchers Cut Error Correction Losses Using New Five-Qubit Code
Quantum Zeitgeist · Oct 9, 2026
Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
arXiv · Sep 2, 2026
Quantum Codes for Generalized Amplitude-damping Noise
arXiv · Sep 15, 2026
Probabilistic Algorithms for Ising Machines from Optimization to Generative AI
arXiv · Oct 6, 2026