Neighborhood Mixture Model for Knowledge Base Completion
This work addresses the challenge of knowledge base completion for natural language processing applications, representing an incremental improvement over existing embedding methods.
The paper tackles the problem of incomplete knowledge bases by introducing a novel entity representation as a mixture of its neighborhood, applied to the TransE embedding model. The result is a significant improvement over TransE, outperforming other state-of-the-art models on three benchmark datasets for triple classification, entity prediction, and relation prediction tasks.
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.