A simple algorithm to learn embeddings of entities in knowledge graph.
What is it
TripleWalk is an algorithm to learn vector embeddings of entities in a knowledge graph by performing random walks on triples.
Installation
Please not this package is only available for python 3.8+ and Pytorch >= 1.9.0
Install from PyPI
pip install triple-walk
Install from Github
pip install git+https://github.com/udel-cbcb/triple_walk.git#egg=triple_walk
Triple Walk
Author : Sachin Gavali
Requirements
1. Pytorch >= 1.9.0
2. NVIDIA-GPU (Cuda Toolkit >= 11.4
3. AMD-GPU (ROCM == 4.0.1)
4. Python == 3.8
Examples
- SkipGram Triple Walk model : SkipGramTriple
- CBOW Triple Walk model : CBOWTriple
Metadata
Release files for triple-walk 0.0.4
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| File | Size | Uploaded | Provenance | Actions |
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| triple_walk-0.0.4.tar.gz | 19.4 kB | Without provenance | Details for triple_walk-0.0.4.tar.gz |
Release files / triple_walk-0.0.4.tar.gz
| Download URL | triple_walk-0.0.4.tar.gz |
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| Size | 19.4 kB |
| Tags | Source |
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twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.25.0 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.54.1 importlib-metadata/4.6.4 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.10
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Security for triple-walk 0.0.4
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