https://github.com/cthoyt/translational-toys

Knowledge graphs representing geometric shapes matched to knowledge graph embedding models to learn them
https://github.com/cthoyt/translational-toys

Last synced: about 1 month ago
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Knowledge graphs representing geometric shapes matched to knowledge graph embedding models to learn them

• Host: GitHub
• URL: https://github.com/cthoyt/translational-toys
• Owner: cthoyt
• License: mit
• Created: 2021-05-06T14:47:35.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-06T17:00:51.000Z (about 3 years ago)
• Last Synced: 2024-06-12T02:34:47.668Z (5 months ago)
• Language: Python
• Homepage:
• Size: 73.6 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# translational-toys

This repository contains workflows for training knowledge graph embedding models

on knowledge graphs representing interesting geometries (generated

by [`geometric_graphs`](https://github.com/cthoyt/geometric-graphs/))

using [PyKEEN](https://github.com/pykeen/pykeen) and animate the evolution of

their entity embeddings. Rerun the examples with

```shell

$ pip install tox

$ tox

```

## Line

A linear dataset embedded with TransE/SoftPlus Loss by

running `python cli.py line`:

  

   

  

## Square Grid in 2D

A square grid dataset embedded with TransE/NSSA Loss by

running `python cli.py squares`:

  

   

  

Additional idea: try training in much higher dimensions, then use ISOMAP to

reduce back down to 2D and see how true it is.

## Hexagonal Grid in 2D

A hexagonal grid dataset embedded with TransE/SoftPlus Loss by

running `python cli.py hexagons`:

  

   

  

**Note** the hexagonal grid shape is not learned if there are no constraints on

the relations because it's easier to learn different sizes, and just create a

square grid. To get this behavior, I ran a modified TransE in which I set the

relation constrainer to `normalize`.

## Circle in 2D

TODO