awslabs/dgl-ke

Health v2 · maintenance only

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1331 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DGL-KE is a high-performance Python package built on Deep Graph Library (DGL) for training, evaluating, and inferring knowledge graph embeddings at scale. It supports popular models like TransE, TransR, RESCAL, DistMult, ComplEx, and RotatE, and scales to graphs with millions of nodes and billions of edges on CPUs, GPUs, or clusters.

Use cases

• train knowledge graph embeddings on large graphs
• compute embeddings for a knowledge graph with billions of edges
• evaluate link prediction quality of KG embeddings
• predict missing entities or relations in a knowledge graph
• run distributed knowledge graph embedding training on a cluster
• find similar entities using embedding similarity

When to choose

• you need to train KG embeddings on very large graphs (millions of nodes, billions of edges) with GPU or distributed support
• you want a CLI-driven workflow for training, evaluating, and predicting with standard KGE models like TransE, DistMult, ComplEx, or RotatE
• you are already using the Deep Graph Library ecosystem

When to avoid

• you only need basic TransE, DistMult, or RotatE training - the AWS GraphStorm project is the recommended successor for those models
• you need general-purpose graph neural networks rather than knowledge graph embeddings
• you need a Python API-first library rather than CLI commands

Facets

library · maturity active

machine-learning cli benchmarking machine-learning graph-processing large-language-models data-science python cloud knowledge-graph-embeddings graph-learning dgl link-prediction distributed-training transe rotate linux gpu

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