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Given a set of documents from a specific domain (e.g., medical researchjournals), how do we automatically build a Knowledge Graph (KG) for thatdomain? Automatic identification of relations and their schemas, i.e., typesignature of arguments of relations (e.g., undergo(Patient, Surgery)), is animportant first step towards this goal. We refer to this problem as RelationSchema Induction (RSI). In this paper, we propose Schema Induction usingCoupled Tensor Factorization (SICTF), a novel tensor factorization method forrelation schema induction. SICTF factorizes Open Information Extraction(OpenIE) triples extracted from a domain corpus along with additional sideinformation in a principled way to induce relation schemas. To the best of ourknowledge, this is the first application of tensor factorization for the RSIproblem. Through extensive experiments on multiple real-world datasets, we findthat SICTF is not only more accurate than state-of-the-art baselines, but alsosignificantly faster (about 14x faster).

Relation Schema Induction using Tensor Factorization with Side Information