Relation Schema Induction using Tensor Factorization with Side Information

Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically identify the schema of relations, i.e., type signature of arguments of relations (e.g., undergo(Patient, Surgery)) – a necessary first step towards building a Knowledge Graph (KG) out of the given set of documents? We refer to this problem as Relation Schema Induction (RSI). While Open Information Extraction (OIE) techniques aim at extracting surface-level text triples of the form (John, underwent, Angioplasty), they don’t induce the yet unknown schema of the relations themselves. Tensors provide a natural representation for such triples, and factorization of such tensors provide a plausible solution for the RSI problem. To the best of our knowledge, tensor factorization methods have not been used for the RSI problem. We fill this gap and propose Coupled Non-Negative Tensor Factorization (CNTF), a tensor factorization method which is able to incorporate additional side information in a principled way for more effective Relation Schema Induction. We report our findings on multiple real-world datasets and demonstrate CNTF’s effectiveness over state-of-the-art baselines both in terms of accuracy and speed. We hope to make all datasets and code publicly available upon publication of the paper.