# AmpliGraph
**Repository Path**: mysprings/AmpliGraph
## Basic Information
- **Project Name**: AmpliGraph
- **Description**: Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-05
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 
[](https://doi.org/10.5281/zenodo.2595043)
[](http://ampligraph.readthedocs.io/?badge=latest)
[Join the conversation on Slack](https://join.slack.com/t/ampligraph/shared_invite/enQtNTc2NTI0MzUxMTM5LTRkODk0MjI2OWRlZjdjYmExY2Q3M2M3NGY0MGYyMmI4NWYyMWVhYTRjZDhkZjA1YTEyMzBkMGE4N2RmNTRiZDg)
**Open source library based on TensorFlow that predicts links between concepts in a knowledge graph.**
**AmpliGraph** is a suite of neural machine learning models for relational Learning, a branch of machine learning
that deals with supervised learning on knowledge graphs.
**Use AmpliGraph if you need to**:
* Discover new knowledge from an existing knowledge graph.
* Complete large knowledge graphs with missing statements.
* Generate stand-alone knowledge graph embeddings.
* Develop and evaluate a new relational model.
AmpliGraph's machine learning models generate **knowledge graph embeddings**, vector representations of concepts in a metric space:
It then combines embeddings with model-specific scoring functions to predict unseen and novel links:
## Key Features
* **Intuitive APIs**: AmpliGraph APIs are designed to reduce the code amount required to learn models that predict links in knowledge graphs.
* **GPU-Ready**: AmpliGraph is based on TensorFlow, and it is designed to run seamlessly on CPU and GPU devices - to speed-up training.
* **Extensible**: Roll your own knowledge graph embeddings model by extending AmpliGraph base estimators.
## Modules
AmpliGraph includes the following submodules:
* **Datasets**: helper functions to load datasets (knowledge graphs).
* **Models**: knowledge graph embedding models. AmpliGraph contains **TransE**, **DistMult**, **ComplEx**, **HolE**, **ConvE**, **ConvKB**. (More to come!)
* **Evaluation**: metrics and evaluation protocols to assess the predictive power of the models.
* **Discovery**: High-level convenience APIs for knowledge discovery (discover new facts, cluster entities, predict near duplicates).
## Installation
### Prerequisites
* Linux, macOS, Windows
* Python ≥ 3.7
#### Provision a Virtual Environment
Create and activate a virtual environment (conda)
```
conda create --name ampligraph python=3.7
source activate ampligraph
```
#### Install TensorFlow
AmpliGraph is built on TensorFlow 1.x.
Install from pip or conda:
**CPU-only**
```
pip install "tensorflow>=1.15.2,<2.0"
or
conda install tensorflow'>=1.15.2,<2.0.0'
```
**GPU support**
```
pip install "tensorflow-gpu>=1.15.2,<2.0"
or
conda install tensorflow-gpu'>=1.15.2,<2.0.0'
```
### Install AmpliGraph
Install the latest stable release from pip:
```
pip install ampligraph
```
If instead you want the most recent development version, you can clone the repository
and install from source (your local working copy will be on the latest commit on the `develop` branch).
The code snippet below will install the library in editable mode (`-e`):
```
git clone https://github.com/Accenture/AmpliGraph.git
cd AmpliGraph
pip install -e .
```
### Sanity Check
```python
>> import ampligraph
>> ampligraph.__version__
'1.3.2'
```
## Predictive Power Evaluation (MRR Filtered)
AmpliGraph includes implementations of TransE, DistMult, ComplEx, HolE, ConvE, and ConvKB.
Their predictive power is reported below and compared against the state-of-the-art results in literature.
[More details available here](https://docs.ampligraph.org/en/latest/experiments.html).
| |FB15K-237 |WN18RR |YAGO3-10 | FB15k |WN18 |
|------------------------------|----------|---------|-----------|------------|---------------|
| Literature Best | **0.35***| 0.48* | 0.49* | **0.84**** | **0.95*** |
| TransE (AmpliGraph) | 0.31 | 0.22 | **0.51** | 0.63 | 0.66 |
| DistMult (AmpliGraph) | 0.31 | 0.47 | 0.50 | 0.78 | 0.82 |
| ComplEx (AmpliGraph) | 0.32 | **0.51**| 0.49 | 0.80 | 0.94 |
| HolE (AmpliGraph) | 0.31 | 0.47 | 0.50 | 0.80 | 0.94 |
| ConvE (AmpliGraph) | 0.26 | 0.45 | 0.30 | 0.50 | 0.93 |
| ConvE (1-N, AmpliGraph) | 0.32 | 0.48 | 0.40 | 0.80 | **0.95** |
| ConvKB (AmpliGraph) | 0.23 | 0.39 | 0.30 | 0.65 | 0.80 |
* Timothee Lacroix, Nicolas Usunier, and Guillaume Obozinski. Canonical tensor decomposition for knowledge base
completion. In International Conference on Machine Learning, 2869–2878. 2018.
** Kadlec, Rudolf, Ondrej Bajgar, and Jan Kleindienst. "Knowledge base completion: Baselines strike back.
" arXiv preprint arXiv:1705.10744 (2017).
Results above are computed assigning the worst rank to a positive in case of ties.
Although this is the most conservative approach, some published literature may adopt an evaluation protocol that assigns
the best rank instead.
## Documentation
**[Documentation available here](http://docs.ampligraph.org)**
The project documentation can be built from your local working copy with:
```
cd docs
make clean autogen html
```
## How to contribute
See [guidelines](http://docs.ampligraph.org) from AmpliGraph documentation.
## How to Cite
If you like AmpliGraph and you use it in your project, why not starring the project on GitHub!
[](https://GitHub.com/Accenture/AmpliGraph/stargazers/)
If you instead use AmpliGraph in an academic publication, cite as:
```
@misc{ampligraph,
author= {Luca Costabello and
Sumit Pai and
Chan Le Van and
Rory McGrath and
Nicholas McCarthy and
Pedro Tabacof},
title = {{AmpliGraph: a Library for Representation Learning on Knowledge Graphs}},
month = mar,
year = 2019,
doi = {10.5281/zenodo.2595043},
url = {https://doi.org/10.5281/zenodo.2595043}
}
```
## License
AmpliGraph is licensed under the Apache 2.0 License.