From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction

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.

Authors

• Han Xiao Xiamen University
• Minlie Huang Tsinghua University, Tsinghua University The CoAI Group, DCST, Tsinghua University
• Xiaoyan Zhu Xi'an Jiaotong University

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