Relation Schema Induction using Tensor Factorization with Side Information

TL;DR

This paper introduces SICTF, a novel tensor factorization method that leverages side information to automatically induce relation schemas from domain-specific text, significantly improving accuracy and speed over existing methods.

Contribution

The paper presents the first application of tensor factorization for relation schema induction, incorporating side information to enhance accuracy and efficiency.

Findings

SICTF outperforms state-of-the-art baselines in accuracy.

SICTF is approximately 14 times faster than previous methods.

Extensive experiments validate the effectiveness of SICTF on real-world datasets.

Abstract

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).