Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings

Fuente: arXiv
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Main Authors: Zhu, Yuqicheng, Potyka, Nico, Xiong, Bo, Tran, Trung-Kien, Nayyeri, Mojtaba, Kharlamov, Evgeny, Staab, Steffen
Format: Preprint
Published: 2024
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author Zhu, Yuqicheng
Potyka, Nico
Xiong, Bo
Tran, Trung-Kien
Nayyeri, Mojtaba
Kharlamov, Evgeny
Staab, Steffen
author_facet Zhu, Yuqicheng
Potyka, Nico
Xiong, Bo
Tran, Trung-Kien
Nayyeri, Mojtaba
Kharlamov, Evgeny
Staab, Steffen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2407_11821
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Zhu, Yuqicheng
Potyka, Nico
Xiong, Bo
Tran, Trung-Kien
Nayyeri, Mojtaba
Kharlamov, Evgeny
Staab, Steffen
Artificial Intelligence
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.
title Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
topic Artificial Intelligence
url https://arxiv.org/abs/2407.11821