Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Sujit-O/pykg2vec
Python library for knowledge graph embedding and representation learning.
https://github.com/Sujit-O/pykg2vec
embedding-models embedding-python knowledge-graph representation-learning
Last synced: about 7 hours ago
JSON representation
Python library for knowledge graph embedding and representation learning.
- Host: GitHub
- URL: https://github.com/Sujit-O/pykg2vec
- Owner: Sujit-O
- License: mit
- Created: 2019-03-06T23:06:11.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-21T16:14:56.000Z (over 3 years ago)
- Last Synced: 2024-11-01T21:36:33.204Z (10 days ago)
- Topics: embedding-models, embedding-python, knowledge-graph, representation-learning
- Language: Python
- Homepage:
- Size: 9.29 MB
- Stars: 606
- Watchers: 13
- Forks: 111
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pykg2vec.readthedocs.io/en/latest/?badge=latest) [](https://circleci.com/gh/Sujit-O/pykg2vec) [](https://www.python.org/downloads/release/python-370/) [](https://travis-ci.org/Sujit-O/pykg2vec) [](https://badge.fury.io/py/pykg2vec) [](https://github.com/Sujit-O/pykg2vec/blob/master/LICENSE) [](https://coveralls.io/github/Sujit-O/pykg2vec?branch=master) [](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FSujit-O%2Fpykg2vec)
# Pykg2vec: Python Library for KGE Methods
Pykg2vec is a library for learning the representation of entities and relations in Knowledge Graphs built on top of PyTorch 1.5 (TF2 version is available in [tf-master](https://github.com/Sujit-O/pykg2vec/tree/tf2-master) branch as well). We have attempted to bring state-of-the-art Knowledge Graph Embedding (KGE) algorithms and the necessary building blocks in the pipeline of knowledge graph embedding task into a single library. We hope Pykg2vec is both practical and educational for people who want to explore the related fields.
Features:
* Support state-of-the-art KGE model implementations and benchmark datasets. (also support custom datasets)
* Support automatic discovery for hyperparameters.
* Tools for inspecting the learned embeddings.
* Support exporting the learned embeddings in TSV or Pandas-supported format.
* Interactive result inspector.
* TSNE-based, KPI summary visualization (mean rank, hit ratio) in various format. (csvs, figures, latex table)
We welcome any form of contribution! Please refer to [CONTRIBUTING.md](https://github.com/Sujit-O/pykg2vec/blob/master/CONTRIBUTING.md) for more details.
## To Get Started
Before using pykg2vec, we recommend users to have the following libraries installed:
* python >=3.7 (recommended)
* pytorch>= 1.5
Quick Guide for Anaconda users:
* Setup a Virtual Environment: we encourage you to use anaconda to work with pykg2vec:
```bash
(base) $ conda create --name pykg2vec python=3.7
(base) $ conda activate pykg2vec
```
* Setup Pytorch: we encourage to use pytorch with GPU support for good training performance. However, a CPU version also runs. The following sample commands are for setting up pytorch:
```bash
# if you have a GPU with CUDA 10.1 installed
(pykg2vec) $ conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
# or cpu-only
(pykg2vec) $ conda install pytorch torchvision cpuonly -c pytorch
```
* Setup Pykg2vec:
```bash
(pykg2vec) $ git clone https://github.com/Sujit-O/pykg2vec.git
(pykg2vec) $ cd pykg2vec
(pykg2vec) $ python setup.py install
```
For beginners, these papers, [A Review of Relational Machine Learning for Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7358050), [Knowledge Graph Embedding: A Survey of Approaches and Applications](https://ieeexplore.ieee.org/document/8047276), and [An overview of embedding models of entities and relationships for knowledge base completion](https://arxiv.org/abs/1703.08098) can be good starting points!
## User Documentation
The documentation is [here](https://pykg2vec.readthedocs.io/).
## Usage Examples
With pykg2vec command-line interface, you can
1. Run a single algorithm with various models and datasets (customized dataset also supported).
```
# Check all tunnable parameters.
(pykg2vec) $ pykg2vec-train -h
# Train TransE on FB15k benchmark dataset.
(pykg2vec) $ pykg2vec-train -mn TransE
# Train using different KGE methods.
(pykg2vec) $ pykg2vec-train -mn [TransE|TransD|TransH|TransG|TransM|TransR|Complex|ComplexN3|
CP|RotatE|Analogy|DistMult|KG2E|KG2E_EL|NTN|Rescal|SLM|SME|SME_BL|HoLE|
ConvE|ConvKB|Proje_pointwise|MuRP|QuatE|OctonionE|InteractE|HypER]
# For KGE using projection-based loss function, use more processes for batch generation.
(pykg2vec) $ pykg2vec-train -mn [ConvE|ConvKB|Proje_pointwise] -npg [the number of processes, 4 or 6]
# Train TransE model using different benchmark datasets.
(pykg2vec) $ pykg2vec-train -mn TransE -ds [fb15k|wn18|wn18_rr|yago3_10|fb15k_237|ks|nations|umls|dl50a|nell_955]
# Train TransE model using your own hyperparameters.
(pykg2vec) $ pykg2vec-train -exp True -mn TransE -ds fb15k -hpf ./examples/custom_hp.yaml
# Use your own dataset
(pykg2vec) $ pykg2vec-train -mn TransE -ds [name] -dsp [path to the custom dataset]
```
2. Tune a single algorithm.
```
# Tune TransE using the benchmark dataset.
(pykg2vec) $ pykg2vec-tune -mn [TransE] -ds [dataset name]
# Tune TransE with your own search space
(pykg2vec) $ pykg2vec-tune -exp True -mn TransE -ds fb15k -ssf ./examples/custom_ss.yaml
```
3. Perform Inference Tasks (more advanced).
```
# Train a model and perform inference tasks.
(pykg2vec) $ pykg2vec-infer -mn TransE
# Perform inference tasks over a pretrained model.
(pykg2vec) $ pykg2vec-infer -mn TransE -ld [path to the pretrained model]
```
\* NB: On Windows, use `pykg2vec-train.exe`, `pykg2vec-tune.exe` and `pykg2vec-infer.exe` instead.
For more usage of pykg2vec APIs, please check the [programming examples](https://pykg2vec.readthedocs.io/en/latest/auto_examples/index.html).
## Citation
Please kindly consider citing our paper if you find pykg2vec useful for your research.
```
@article{yu2019pykg2vec,
title={Pykg2vec: A Python Library for Knowledge Graph Embedding},
author={Yu, Shih Yuan and Rokka Chhetri, Sujit and Canedo, Arquimedes and Goyal, Palash and Faruque, Mohammad Abdullah Al},
journal={arXiv preprint arXiv:1906.04239},
year={2019}
}
```