Multi-Field Knowledge Graph Embedding for Memory Reduction

The patent applies local quality by assigning different embedding dimensions to different node types based on their specific requirements. High-degree nodes receive higher-dimensional embeddings to capture their complex relationships, while low-degree nodes use lower-dimensional embeddings sufficient for their simpler relationship patterns. This differentiated approach maintains link prediction accuracy for all node types while reducing overall memory consumption compared to uniform high-dimensional embeddings.

The patent segments the knowledge graph nodes into multiple categories (high-degree nodes, low-degree nodes, and intermediate nodes) based on their degree distributions. Each segment is then embedded into a separate vector space with appropriately scaled dimensions. This segmentation allows the model to allocate computational and memory resources efficiently across different node populations, resolving the contradiction between accuracy and memory usage.

The patent implements local quality by tailoring embedding dimensions to local node characteristics. Instead of applying a uniform embedding dimension across all nodes, the system dynamically determines the appropriate dimensionality based on each node's degree and relationship patterns. This ensures that high-degree nodes receive sufficient embedding capacity for accurate representation while low-degree nodes use appropriately reduced dimensions, achieving consistent performance across heterogeneous node populations.

The patent introduces dynamics into the embedding architecture by making the embedding dimensionality adaptive rather than static. The system dynamically adjusts the embedding dimension for each node based on its degree distribution and relationship complexity during the embedding process. This dynamic approach allows the model to maintain consistent performance across nodes of varying degrees while avoiding the oversimplification inherent in fixed-dimension approaches.