Neighborhood Mixture Model for Knowledge Base Completion

Abstract

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.

V1: In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016. V2: Corrected citation to (Krompaß et al., 2015). V3: A revised version of our CoNLL 2016 paper to update latest related work