Rethinking Identification and Semantic Meanings: Difference-based Knowledge Graph Embedding

Knowledge graph embedding (KGE) encodes entities and relations as low-dimensional representations and relies on model architectures and scoring functions to capture link patterns, such as symmetry and inversion. However, existing methods directly use the embedding of each entity or relation for identification and as its semantic meaning. We challenge this from a difference-based perspective, which emphasize that semantic meaning is not intrinsic or self-identical, but instead arises from systematic differences among representations. Motivated by this, we propose a simple yet effective representation-level transformation that redefines the semantic representation of an entity or relation as its embedding differences with respect to all other entities or relations. This transformation can be seamlessly applied as a plug-in to a wide range of state-of-the-art KGE models, preserving overall computational efficiency while consistently improving link prediction performance across multiple benchmark datasets.