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https://github.com/complexdb/zincbase

A state of the art knowledge base
https://github.com/complexdb/zincbase

graph-embedding knowledge-base knowledge-graph prolog

Last synced: about 1 month ago
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A state of the art knowledge base

Host: GitHub
URL: https://github.com/complexdb/zincbase
Owner: complexdb
License: mit
Created: 2020-01-30T04:54:46.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2020-06-01T02:30:25.000Z (about 4 years ago)
Last Synced: 2024-05-20T09:20:45.408Z (about 2 months ago)
Topics: graph-embedding, knowledge-base, knowledge-graph, prolog
Language: Python
Size: 11.7 MB
Stars: 170
Watchers: 4
Forks: 24
Open Issues: 6
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING
- License: LICENSE

Lists

jimsghstars - complexdb/zincbase - A state of the art knowledge base (Python)

README

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ZincBase is a state of the art knowledge base and complex simulation suite. It does the following:

* Store and retrieve graph structured data efficiently.

* Provide ways to query the graph, including via bleeding-edge graph neural networks.

* Simulate complex effects playing out across the graph and see how predictions change.

Zincbase exists to answer questions like "what is the probability that Tom likes LARPing", or "who likes LARPing", or "classify people into LARPers vs normies", or simulations like "what happens if all the LARPers become normies".



It combines the latest in neural networks with symbolic logic (think expert systems and prolog), graph search, and complexity theory.

View full documentation [here](https://zincbase.readthedocs.io).

## Quickstart

`pip3 install zincbase`

```

from zincbase import KB

kb = KB()

kb.store('eats(tom, rice)')

for ans in kb.query('eats(tom, Food)'):

    print(ans['Food']) # prints 'rice'

...

# The included assets/countries_s1_train.csv contains triples like:

# (namibia, locatedin, africa)

# (lithuania, neighbor, poland)

kb = KB()

kb.from_csv('./assets/countries_s1_train.csv', delimiter='\t')

kb.build_kg_model(cuda=False, embedding_size=40)

kb.train_kg_model(steps=8000, batch_size=1, verbose=False)

kb.estimate_triple_prob('fiji', 'locatedin', 'melanesia')

0.9607

```

# Requirements

* Python 3

* Libraries from requirements.txt

* GPU preferable for large graphs but not required

# Installation

`pip install -r requirements.txt`

_Note:_ Requirements might differ for PyTorch depending on your system.

# Web UI

Zincbase can serve live-updating force-directed graphs in 3D to a web browser. The command

`python -m zincbase.web` will set up a static file server and a websocket

server for live updates. Visit `http://localhost:5000/` in your browser

and you'll see the graph UI. As you build a graph in Python, you can

visualize it (and changes to it) in realtime through this UI.

Here are a couple of examples (source code [here](https://github.com/complexdb/zincbase/tree/master/examples/visualization)):

![Peek 2020-03-21 12-34](https://user-images.githubusercontent.com/2245347/77235135-93e52a80-6b70-11ea-89ca-fe01f83708ff.gif)

![Peek 2020-03-21 12-39](https://user-images.githubusercontent.com/2245347/77235199-0f46dc00-6b71-11ea-8380-6698283c98f6.gif)

# Complexity (Graph/Network) Examples

Two such examples are included (right now; we intend to include more soon such

as virus spread and neural nets that communicate.) The examples are

basic ones: Conway's Game of Life and the Abelian Sandpile. Here are some

screencaps; source code is [here](https://github.com/complexdb/zincbase/tree/master/examples),

performance can be lightning fast depending how you tweak Zincbase recursion

and propagation settings.

![Peek 2020-03-06 23-53](https://user-images.githubusercontent.com/2245347/76139614-94c17c80-6006-11ea-8690-9059cd1a4672.gif)

![Peek 2020-03-06 23-55](https://user-images.githubusercontent.com/2245347/76139591-4a400000-6006-11ea-96fc-ab3943834886.gif)

### Required for the UI

* You should `pip install zincbase[web]` to get the optional web extra.

* You should have Redis running; by default, at `localhost:6379`. This

is easily achievable, just do `docker run -p 6379:6379 -d redis`

# Testing

```

python test/test_main.py

python test/test_graph.py

... etc ... all the test files there

python -m doctest zincbase/zincbase.py

```

# Validation

"Countries" and "FB15k" datasets are included in this repo.

There is a script to evaluate that ZincBase gets at least as good

performance on the Countries dataset as the original (2019) RotatE paper. From the repo's

root directory:

```

python examples/eval_countries_s3.py

```

It tests the hardest Countries task and prints out the AUC ROC, which should be

~ 0.95 to match the paper. It takes about 30 minutes to run on a modern GPU.

There is also a script to evaluate performance on FB15k: `python examples/fb15k_mrr.py`.

## Running the web UI

There are a couple of extra requirements -- install with `pip3 install zincbase[web]`.

You also need an accessible Redis instance somewhere. This one-liner will get it running

locally: `docker run -p 6379:6379 -d redis` (requires Docker, of course.)

You then need a Zincbase server instance running:

## Building documentation

From docs/ dir: `make html`. If something changed a lot: `sphinx-apidoc -o . ..`

## Pushing to pypi

NOTE: This is now all automatic via CircleCI, but here are the manual steps for reference:

* Edit `setup.py` as appropriate (probably not necessary)

* Edit the version in `zincbase/__init__.py`

* From the top project directory `python setup.py sdist bdist_wheel --universal`

* `twine upload dist/*`

# TODO

* add ability to `kb = KB(backend='complexdb://my_api_key')`

* utilize postgres as backend triple store

* Reinforcement learning for graph traversal.

* Rete algorithm (maybe)

# References & Acknowledgements

[Theo Trouillon. Complex-Valued Embedding Models for Knowledge Graphs. Machine Learning[cs.LG]. Université Grenoble Alpes, 2017. English. ffNNT : 2017GREAM048](https://tel.archives-ouvertes.fr/tel-01692327/file/TROUILLON_2017_archivage.pdf)

[L334: Computational Syntax and Semantics -- Introduction to Prolog, Steve Harlow](http://www-users.york.ac.uk/~sjh1/courses/L334css/complete/complete2li1.html)

[Open Book Project: Prolog in Python, Chris Meyers](http://www.openbookproject.net/py4fun/prolog/intro.html)

[Prolog Interpreter in Javascript](https://curiosity-driven.org/prolog-interpreter)

[RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space, Zhiqing Sun and Zhi-Hong Deng and Jian-Yun Nie and Jian Tang, International Conference on Learning Representations, 2019](https://openreview.net/forum?id=HkgEQnRqYQ)

# Citing

If you use this software, please consider citing:

```

@software{zincbase,

  author = {{Tom Grek}},

  title = {ZincBase: A state of the art knowledge base},

  url = {https://github.com/tomgrek/zincbase},

  version = {0.1.1},

  date = {2019-05-12}

}

```

# Contributing

See CONTRIBUTING. And please do!