Title: Rethinking Identity in Knowledge Graph  Embedding. Abstract: Knowledge Graph Embedding (KGE) is a common method to complete real-world Knowledge Graphs (KGs) by learning the embeddings of entities and relations.
Beyond specific KGE models, previous work proposes a general framework based on group. A group has a special element identity that uniquely corresponds to the relation identity in KGs, which implies that identity should be represented uniquely. However, we find that this uniqueness cannot be modeled by bilinear based models, revealing the crack between the framework and models. To this end, we study the required conditions and propose a solution named Unit Ball Bilinear Model (UniBi). In addition to the theoretical superiority, UniBi is more robust and interpretable. Experiments demonstrate that UniBi models the uniqueness without the cost of performance and verify its a robustness and interpretability. 