  Given a set of documents from a specific domain (e.g., medical research
journals), how do we automatically build a Knowledge Graph (KG) for that
domain? Automatic identification of relations and their schemas, i.e., type
signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an
important first step towards this goal. We refer to this problem as Relation
Schema Induction (RSI). In this paper, we propose Schema Induction using
Coupled Tensor Factorization (SICTF), a novel tensor factorization method for
relation schema induction. SICTF factorizes Open Information Extraction
(OpenIE) triples extracted from a domain corpus along with additional side
information in a principled way to induce relation schemas. To the best of our
knowledge, this is the first application of tensor factorization for the RSI
problem. Through extensive experiments on multiple real-world datasets, we find
that SICTF is not only more accurate than state-of-the-art baselines, but also
significantly faster (about 14x faster).
