STransE: a novel embedding model of entities and relationships in knowledge bases
This work addresses the problem of incomplete knowledge bases for natural language processing tasks, but it is incremental as it builds on existing models.
The paper tackles knowledge base completion by proposing STransE, a new embedding model that combines SE and TransE, achieving better link prediction performance on two benchmark datasets.
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. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.