STransE: a novel embedding model of entities and relationships in knowledge bases

Understand

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.

• However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true.
• This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector.
• STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models.