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@lysander07@sigmoid.social
2025-07-30 15:15:32

One of our final topics in the #ISE2025 lecture were Knowledge Graph Embeddings. How to vectorise KG structures while preserving their inherent semantics?
#AI #KGE

@lysander07@sigmoid.social
2025-07-30 15:15:32

One of our final topics in the #ISE2025 lecture were Knowledge Graph Embeddings. How to vectorise KG structures while preserving their inherent semantics?
#AI #KGE

4. Basic Machine Learning / 4.9 Knowledge Graph Embeddings The slide visualises the process of knowledge graph embeddings creation. starting out with a kg, scoring function, loss function, and negatives generation are represented, which are responsible for preserving semantics in the process of vectorisation. The created vectors then serve as representations for entities and properties in downstream tasks, as e.g. classification or question answering.