# AmpliGraph **Repository Path**: donfar/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**: 2020-06-17 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # ![AmpliGraph](docs/img/ampligraph_logo_transparent_300.png) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2595043.svg)](https://doi.org/10.5281/zenodo.2595043) [![Documentation Status](https://readthedocs.org/projects/ampligraph/badge/?version=latest)](http://ampligraph.readthedocs.io/?badge=latest) [Join the conversation on Slack](https://join.slack.com/t/ampligraph/shared_invite/enQtNTc2NTI0MzUxMTM5LTRkODk0MjI2OWRlZjdjYmExY2Q3M2M3NGY0MGYyMmI4NWYyMWVhYTRjZDhkZjA1YTEyMzBkMGE4N2RmNTRiZDg) ![](docs/img/slack_logo.png) **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: ![](docs/img/kg_lp_step1.png) It then combines embeddings with model-specific scoring functions to predict unseen and novel links: ![](docs/img/kg_lp_step2.png) ## 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 ≥ 3.6 #### 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>=1.14.0,<2.0" or conda install tensorflow'>=1.14.0,<2.0.0' ``` **GPU support** ``` pip install "tensorflow-gpu>=1.14.0,<2.0" or conda install tensorflow-gpu'>=1.14.0,<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__ '1.3.1' ``` ## 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). ## 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! [![GitHub stars](https://img.shields.io/github/stars/Accenture/AmpliGraph.svg?style=social&label=Star&maxAge=3600)](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.