From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction

Abstract

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 prediction. There are two reasons: being an ill-posed algebraic system and applying an overstrict geometric form. As precise prediction is critical, we propose an manifold-based embedding principle (\textbf{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.

arXiv admin note: text overlap with arXiv:1509.05488

References in corpus (1)

• Modeling Relation Paths for Representation Learning of Knowledge Bases

Cited by in corpus (4)

• Review on Learning and Extracting Graph Features for Link Prediction
• Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
• SSNE: Effective Node Representation for Link Prediction in Sparse Networks
• Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes