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.

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):

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, performance can be lightning fast depending how you tweak Zincbase recursion and propagation settings.

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

L334: Computational Syntax and Semantics -- Introduction to Prolog, Steve Harlow

Open Book Project: Prolog in Python, Chris Meyers

Prolog Interpreter in Javascript

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

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!

complexdb / zincbase