Python library for Representation Learning on Knowledge Graphs
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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:

AmpliGraph 2.0.0 is now available!

The new version features TensorFlow 2 back-end and Keras style APIs that makes it faster, easier to use and extend the support for multiple features. Further, the data input/output pipeline has changed, and the support for some obsolete models was discontinued.
See the Changelog for a more thorough list of changes.

Key Features

  • Intuitive APIs: AmpliGraph APIs are designed to reduce the code amount required to learn models that predict links in knowledge graphs. The new version AmpliGraph 2 APIs are in Keras style, making the user experience even smoother.
  • GPU-Ready: AmpliGraph 2 is based on TensorFlow 2, 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.


AmpliGraph includes the following submodules:

  • Datasets: helper functions to load datasets (knowledge graphs).
  • Models: knowledge graph embedding models. AmpliGraph 2 contains TransE, DistMult, ComplEx, HolE (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).
  • Compat: submodule that extends the compatibility of AmpliGraph 2 APIs to those of AmpliGraph 1.x for the user already familiar with them.



  • Linux, macOS, Windows
  • Python 3.8

Provision a Virtual Environment

Create and activate a virtual environment (conda)

conda create --name ampligraph python=3.8
source activate ampligraph

Install TensorFlow

AmpliGraph 2 is built on TensorFlow 2.x. Install from pip or conda:


pip install "tensorflow>=2.9"


conda install tensorflow'>=2.9'

Install TensorFlow 2 for Mac OS M1 chip

conda install -c apple tensorflow-deps
pip install --user tensorflow-macos==2.10
pip install --user tensorflow-metal==0.6

In case of problems with installation refer to Tensorflow Plugin page on Apple developer site.

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
cd AmpliGraph
pip install -e .

Sanity Check

>>> import ampligraph
>>> ampligraph.__version__

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.

FB15K-237 WN18RR YAGO3-10 FB15k WN18
Literature Best 0.35* 0.48* 0.49* 0.84** 0.95*
TransE (AmpliGraph 2) 0.31 0.22 0.50 0.62 0.64
DistMult (AmpliGraph 2) 0.30 0.47 0.48 0.71 0.82
ComplEx (AmpliGraph 2) 0.31 0.50 0.49 0.73 0.94
HolE (AmpliGraph 2) 0.30 0.47 0.47 0.73 0.94
TransE (AmpliGraph 1) 0.31 0.22 0.51 0.63 0.66
DistMult (AmpliGraph 1) 0.31 0.47 0.50 0.78 0.82
ComplEx (AmpliGraph 1) 0.32 0.51 0.49 0.80 0.94
HolE (AmpliGraph 1) 0.31 0.47 0.50 0.80 0.94
ConvE (AmpliGraph 1) 0.26 0.45 0.30 0.50 0.93
ConvE (1-N, AmpliGraph 1) 0.32 0.48 0.40 0.80 0.95
ConvKB (AmpliGraph 1) 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, 28692878. 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 available here

The project documentation can be built from your local working copy with:

cd docs
make clean autogen html

How to contribute

See guidelines 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

If you instead use AmpliGraph in an academic publication, cite as:

 author= {Luca Costabello and
          Alberto Bernardi and
          Adrianna Janik 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   = {}


AmpliGraph is licensed under the Apache 2.0 License.

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