ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
What it is
This is the official PyTorch implementation of the ConE model, a knowledge graph embedding method that represents entities as cones in a hyperbolic space to support multi-hop reasoning. It works by learning geometric embeddings that preserve hierarchical structures, enabling the model to predict missing links in knowledge graphs through cone-based operations.
The problem it solves
Standard Euclidean embeddings struggle to represent hierarchical and tree-like structures in knowledge graphs, leading to poor performance in multi-hop reasoning tasks.
What you could use it for
- Multi-hop reasoning over knowledge graphs
- Knowledge graph completion and link prediction
- Representing hierarchical data in hyperbolic space
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.