Relation Schema Induction using Tensor Factorization with Side Information
This addresses the challenge of automatic relation schema induction for domain-specific Knowledge Graph construction, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of automatically building Knowledge Graphs by proposing SICTF, a tensor factorization method for Relation Schema Induction, which is more accurate and about 14x faster than state-of-the-art baselines in experiments on real-world datasets.
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).