From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction / 1315
Han Xiao, Minlie Huang, Xiaoyan Zhu
Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise link prediction. There are two reasons: being an ill-posed algebraic system and adopting an overstrict geometric form. As precise link prediction is critical, we propose a manifold-based embedding principle (ManifoldE) which could be treated as a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold in ours. Extensive experiments show that the proposed models achieve substantial improvements against the state-of-the-art baselines especially for the precise prediction task, and yet maintain high efficiency.