# Auto-encoding a Knowledge Graph Using a Deep Belief Network: A Random Fields Perspective

arXiv: https://arxiv.org/abs/1911.06322 · HTML: https://mlsanity.com/paper/1911.06322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories](https://mlsanity.com/paper/2104.10762/) (2021)

## Abstract (arXiv)

We started with a knowledge graph of connected entities and descriptive properties of those entities, from which, a hierarchical representation of the knowledge graph is derived. Using a graphical, energy-based neural network, we are able to show that the structure of the hierarchy can be internally captured by the neural network, which allows for efficient output of the underlying equilibrium distribution from which the data are drawn.