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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
| Date | Stars |
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| 2026-07-24 | 2238 |
| 2026-07-25 | 2238 |
| 2026-07-28 | 2238 |
| 2026-07-30 | 2238 |
| 2026-07-31 | 2238 |
| 2026-08-06 | 2238 |
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#  [](https://doi.org/10.5281/zenodo.2595043) [](http://ampligraph.readthedocs.io/?badge=latest) [](https://dl.circleci.com/status-badge/redirect/gh/Accenture/AmpliGraph/tree/main) [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:  ## 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.<br /> 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. ## Modules 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. ## Installation ### Prerequisites * Linux, macOS, Windows * Python 3.9 – 3.11 ## Install AmpliGraph To install the latest stable release from pip: ``` pip install ampligraph ``` To sanity check the installation, run the following: ```python >>> import ampligraph >>> ampligraph.__version__ '2.2.0' ``` If instead you want the most recent development version, you can clone the repository from [GitHub](https://github.com/Accenture/AmpliGraph.git), install AmpliGraph from source and checkout the `develop` branch. In this way, your local working copy will be on the latest commit on the `develop` branch. ``` git clone https://github.com/Accenture/AmpliGraph.git cd AmpliGraph git checkout develop uv sync ``` To sanity check the installation, run the following: ```python >>> import ampligraph >>> ampligraph.__version__ '2.2-dev' ``` ## Predictive Power Evaluation (MRR Filtered) AmpliGraph includes implementations of TransE,
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f869bb84e68b2733, topic:representation-learning, desc:representation learning
matched fp:f869bb84e68b2733, topic:knowledge-graph, readme:knowledge graph