Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
Join the conversation on Slack **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. 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. 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. 11 Install AmpliGraph To install the latest stable release from pip: To sanity check the installation, run the following: If instead you want the most recent development version, you can clone the repository from GitHub, install AmpliGraph from source and checkout the branch. In this way, your local working copy will be on the latest commit on the branch. To sanity check the installation, run the following: Predictive Power Evaluation
(MRR Filtered) AmpliGraph includes implementations of TransE, DistMult, ComplEx, HolE and RotatE. Versions Timothee Lacroix, Nicolas Usunier, and Guillaume Obozinski. Canonical tensor decomposition for knowledge base completion. In International Conference on Machine Learning, 2869–2878. ** Kadlec, Rudolf, Ondrej Bajgar, and Jan Kleindienst. "Knowledge base completion: Baselines strike back. 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** The project documentation can be built from your local working copy with
Frequently asked questions
How many stars and forks are recorded?
Accenture/AmpliGraph has 2,242 stars and 257 forks.
What is the repository rank?
Accenture/AmpliGraph is ranked 8,821.
What language and category are recorded?
Accenture/AmpliGraph has 2,242 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
Accenture/AmpliGraph is compared with azavea/raster-vision, ranked 8,820 with 2,243 stars.
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.
Modules
AmpliGraph includes the following submodules:
Datasets: helper functions to load datasets (knowledge graphs).
If instead you want the most recent development version, you can clone the repository from
GitHub, 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:
AmpliGraph includes implementations of TransE, DistMult, ComplEx, HolE and RotatE. Versions <2.0 also includes 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
0.31
0.22
0.50
0.62
0.66
DistMult
0.30
0.47
0.48
0.71
0.82
ComplEx
0.31
0.51
0.49
0.73
0.94
HolE
0.30
0.47
0.47
0.73
0.94
RotatE
0.31
0.51
0.43
0.70
0.95
ConvE (AmpliGraph v1.4)
0.26
0.45
0.30
0.50
0.93
ConvE (1-N, AmpliGraph v1.4)
0.32
0.48
0.40
0.80
0.95
ConvKB (AmpliGraph v1.4)
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.
If you like AmpliGraph and you use it in your project, why not starring the project on GitHub!
If you instead use AmpliGraph in an academic publication, cite as:
@misc{ampligraph,
author= {Luca Costabello and
Alberto Bernardi and
Adrianna Janik and
Aldan Creo 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.
