awslabs / dgl-ke
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Why this job: works in the areas we cover, but does not say which job it does
What it does
Knowledge graphs (KGs) are data structures that store information about different entities (nodes) and their relations (edges). A common approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings. DGL-KE is a high performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. The package is implemented on the top of *Deep Graph Library (DGL)* and developers can run DGL-KE on CPU machine, GPU machine, as well as clusters with a set of popular models, including TransE, TransR, RESCAL, DistMult, ComplEx, and RotatE.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 4 months ago
- Stars
- 1,336
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- 0.1.1
- Released 6 years ago
- Built with
- Python
- 88% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 61% closed
- 57 still open
- Pull requests
- 120 merged
- 3 waiting
- Releases
- 2
- 0 in the last year
More: languages, topics and project record
Languages
- Python 88%
- Jupyter Notebook 8%
- Shell 4%
Topics
- machine-learning
- knowledge-graph
- knowledge-graphs-embeddings
- graph-learning
- dgl
Project record
- JobOther AI tools
- StatusListed
- Commits241
- Forks202
- Watching24
- Stars in 28 days0
- CreatedMar 3, 2020
- First seen by usNov 7, 2026
- CheckedNov 8, 2026