A re-ranking model for accurate knowledge base completion with knowledge base schema and web statistic

Abstract

Knowledge base completion aims to complete a knowledge base by filling up missing facts of the knowledge base. Neural knowledge base embeddings proposed to solve this task measure the plausibility of all candidate triples, and then select top-ranked triples by the plausibility as new facts for the knowledge base. The plausibility by neural embeddings allows true facts to be ranked at high positions, but not at top positions. This is because neural knowledge base embeddings are limited to using only the information within the knowledge base. Therefore, this paper proposes a re-ranking model for precise knowledge base completion. As a re-ranking model, a neural network which uses knowledge base schema and web statistic additionally is adopted. As a result, the proposed re-ranking model has an effect of using additional information for knowledge base completion. Thus, the candidate triples are first ranked by a neural knowledge base embedding, and then the result is re-ranked by the neural network. The experimental results show that the proposed re-ranking model improves the base neural embeddings up to 16% in Hits@1. This implies that the re-ranking model places true facts at top positions effectively.