In contrast, CNN based models learn more expressive embeddings due to their parameter efficiency and consideration of complex relations.
both translational and CNN based models process each triple independently and hence fail to encapsulate the semantically rich and latent relations that are inherently present in the vicinity of a given entity in a KG.