Knowledge Fusion via Joint Tensor and Matrix Factorization

We consider the task of knowledge fusion, an important aspect of cognitive intelligence, with the goal of combining part-of knowledge drawn from different sources. For this, entities and relations are cast into matrix-based representations. Unlike previous work on relation prediction, we consider the challenging setting of graphs with large amounts of completely separate connected components and no overlap between the training and test set entities. In order to address these challenges, we propose a novel cognitively inspired factorization method that jointly factorizes a subject–predicate–object tensor via RESCAL and a similarity matrix via matrix factorization. Our experimental results show that our method significantly outperforms several strong baseline models, including RESCAL and several TransE-style models. The proposed joint factorization of a subject–predicate–object tensor while applying matrix factorization to a similarity matrix obtains substantially higher average accuracy rates than previous approaches. This shows that it can successfully address the challenge of knowledge fusion of disconnected data.