Knowledge Base Completion Using Matrix Factorization

With the development of Semantic Web, the automatic construction of large scale knowledge bases (KBs) has been receiving increasing attention in recent years. Although these KBs are very large, they are still often incomplete. Many existing approaches to KB completion focus on performing inference over a single KB and suffer from the feature sparsity problem. Moreover, traditional KB completion methods ignore complementarity which exists in various KBs implicitly. In this paper, we treat KBs completion as a large matrix completion task and integrate different KBs to infer new facts simultaneously. We present two improvements to the quality of inference over KBs. First, in order to reduce the data sparsity, we utilize the type consistency constraints between relations and entities to initialize negative data in the matrix. Secondly, we incorporate the similarity of relations between different KBs into matrix factorization model to take full advantage of the complementarity of various KBs. Experimental results show that our approach performs better than methods that consider only existing facts or only a single knowledge base, achieving significant accuracy improvements in binary relation prediction.