{ "info": { "author": "Tom Grek", "author_email": "tom.grek@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "[![CircleCI](https://circleci.com/gh/tomgrek/zincbase.svg?style=svg)](https://circleci.com/gh/tomgrek/zincbase)\n[![DOI](https://zenodo.org/badge/183831265.svg)](https://zenodo.org/badge/latestdoi/183831265)\n[![Documentation Status](https://readthedocs.org/projects/zincbase/badge/?version=latest)](https://zincbase.readthedocs.io/en/latest/?badge=latest)\n\n\"Zincbase\n\nZincbase is a batteries-included kit for building knowledge bases. It exists to do the following:\n\n* Extract facts (aka triples and rules) from unstructured data/text\n* Store and retrieve those facts efficiently\n* Build them into a graph\n* Provide ways to query the graph, including via bleeding-edge graph neural networks.\n\nZincbase exists to answer questions like \"what is the probability that Tom likes LARPing\", or \"who likes LARPing\", or \"classify people into LARPers vs normies\":\n\n\"Example\n\nIt combines the latest in neural networks with symbolic logic (think expert systems and prolog) and graph search.\n\nView full documentation [here](https://zincbase.readthedocs.io).\n\n## Quickstart\n\n```\nfrom zincbase import KB\nkb = KB()\nkb.store('eats(tom, rice)')\nfor ans in kb.query('eats(tom, Food)'):\n print(ans['Food']) # prints 'rice'\n\n...\n# The included assets/countries_s1_train.csv contains triples like:\n# (namibia, locatedin, africa)\n# (lithuania, neighbor, poland)\n# Note that it won't be included if you pip install, only if you git clone.\n\nkb = KB()\nkb.from_csv('./assets/countries.csv')\nkb.build_kg_model(cuda=False, embedding_size=40)\nkb.train_kg_model(steps=2000, batch_size=1, verbose=False)\nkb.estimate_triple_prob('fiji', 'locatedin', 'melanesia')\n0.8467\n```\n\n# Requirements\n\n* Python 3\n* Libraries from requirements.txt\n* GPU preferable for large graphs but not required\n\n# Installation\n\n`pip install zincbase`\n\nThis won't get you the examples or the assets (except those which are automatically\ndownloaded as needed, such as the NER model.) Advanced users may instead wish to:\n\n```\ngit clone https://github.com/tomgrek/zincbase.git\npip install -r requirements.txt\n```\n\n_Note:_ Requirements might differ for PyTorch depending on your system. On Mac OSX \nyou might need to `brew install libomp` first.\n\n# Testing\n\n```\npython -m doctest zincbase/zincbase.py\npython test/test_main.py\npython test/test_graph.py\npython test/test_lists.py\npython test/test_nn_basic.py\npython test/test_nn.py\npython test/test_neg_examples.py\npython test/test_truthiness.py\n```\n\n# Validation\n\n\"Countries\" and \"FB15k\" datasets are included in this repo.\n\nThere is a script to evaluate that ZincBase gets at least as good\nperformance on the Countries dataset as the original (2019) RotatE paper. From the repo's\nroot directory:\n\n```\npython examples/eval_countries_s3.py\n```\n\nIt tests the hardest Countries task and prints out the AUC ROC, which should be\n~ 0.95 to match the paper. It takes about 30 minutes to run on a modern GPU.\n\nThere is also a script to evaluate performance on FB15k: `python examples/fb15k_mrr.py`.\n\n## Building documentation\n\nFrom docs/ dir: `make html`. If something changed a lot: `sphinx-apidoc -o . ..`\n\n## Building the pypi wheel\n\nFrom the repo's root dir:\n\n```\npython setup.py sdist\ntwine upload dist/*\n```\n\n# TODO\n\n* Add documentation\n* to_csv method\n* utilize postgres as backend triple store\n* The to_csv/from_csv methods do not yet support node attributes.\n* Add relation extraction from arbitrary unstructured text\n* Add context to triple - that is interpreted by BERT/ULM/GPT-2 similar and\nput into an embedding that's concat'd to the KG embedding.\n* Reinforcement learning for graph traversal.\n\n# References & Acknowledgements\n\n[Theo Trouillon. Complex-Valued Embedding Models for Knowledge Graphs. Machine Learning[cs.LG]. Universit\u00e9 Grenoble Alpes, 2017. English. ffNNT : 2017GREAM048](https://tel.archives-ouvertes.fr/tel-01692327/file/TROUILLON_2017_archivage.pdf)\n\n[L334: Computational Syntax and Semantics -- Introduction to Prolog, Steve Harlow](http://www-users.york.ac.uk/~sjh1/courses/L334css/complete/complete2li1.html)\n\n[Open Book Project: Prolog in Python, Chris Meyers](http://www.openbookproject.net/py4fun/prolog/intro.html)\n\n[Prolog Interpreter in Javascript](https://curiosity-driven.org/prolog-interpreter)\n\n[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)\n\n# Citing\n\nIf you use this software, please consider citing:\n\n```\n@software{zincbase,\n author = {{Tom Grek}},\n title = {ZincBase: A state of the art knowledge base},\n url = {https://github.com/tomgrek/zincbase},\n version = {0.1.1},\n date = {2019-05-12}\n}\n\n```\n\n# Contributing\n\nSee CONTRIBUTING. 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