  The task of link prediction for knowledge graphs is to predict missing
relationships between entities. Knowledge graph embedding, which aims to
represent entities and relations of a knowledge graph as low dimensional
vectors in a continuous vector space, has achieved promising predictive
performance. If an embedding model can cover different types of connectivity
patterns and mapping properties of relations as many as possible, it will
potentially bring more benefits for link prediction tasks. In this paper, we
propose a novel embedding model, namely LineaRE, which is capable of modeling
four connectivity patterns (i.e., symmetry, antisymmetry, inversion, and
composition) and four mapping properties (i.e., one-to-one, one-to-many,
many-to-one, and many-to-many) of relations. Specifically, we regard knowledge
graph embedding as a simple linear regression task, where a relation is modeled
as a linear function of two low-dimensional vector-presented entities with two
weight vectors and a bias vector. Since the vectors are defined in a real
number space and the scoring function of the model is linear, our model is
simple and scalable to large knowledge graphs. Experimental results on multiple
widely used real-world datasets show that the proposed LineaRE model
significantly outperforms existing state-of-the-art models for link prediction
tasks.
