A Three-Way Model for Collective Learning on Multi-Relational Data

TLDR

Relational learning is becoming increasingly important in many areas of application. We present a novel relational learning approach based on three‑way tensor factorization. The method performs collective learning through latent components and offers an efficient factorization algorithm, validated by experiments on a new dataset and a standard entity‑resolution dataset. Our approach achieves on‑par or better results than state‑of‑the‑art relational learning methods on benchmark datasets, while being significantly faster to compute, and it enables collective learning via latent components.

Abstract

Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.