Open source Python library that predicts links between concepts in a knowledge graph.

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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.


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).

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
           Sumit Pai and
           Chan Le Van and
           Rory McGrath and
           Nick McCarthy and
           Pedro Tabacof},
  title = {{AmpliGraph: a Library for Representation Learning on Knowledge Graphs}},
  month = mar,
  year  = 2019,
  doi   = {10.5281/zenodo.2595043},
  url   = {}