Knowledge Graph Embeddings


Knowledge graph embeddings are low-dimensional vectors that capture the network structure as well as the semantics of the entities and relationships. Standard graph embeddings focus on preserving the network structure only. This way, knowledge graph embeddings are different from graph embeddings.

Embeddings are required for a graph's numerical representation so we can use them as input to machine learning methods. There are several ways to generate knowledge graph embeddings, and they can be broadly divided into three parts:

  • Translation-based methods

  • Factorization-based methods

  • Neural network-based methods

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