Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery

TL;DR

This paper introduces an unsupervised method combining Restricted Boltzmann Machines and neural networks to improve latent truth discovery by accurately estimating source reliability and statement plausibility, even with limited data.

Contribution

It presents a novel scalable model that incorporates source features, generalizes reliability estimation, and outperforms existing methods in latent truth discovery.

Findings

Outperforms state-of-the-art methods significantly

Accurately estimates reliability with few claims

Incorporates arbitrary source and claim features

Abstract

Latent truth discovery, LTD for short, refers to the problem of aggregating ltiple claims from various sources in order to estimate the plausibility of atements about entities. In the absence of a ground truth, this problem is highly challenging, when some sources provide conflicting claims and others no claims at all. In this work we provide an unsupervised stochastic inference procedure on top of a model that combines restricted Boltzmann machines with feed-forward neural networks to accurately infer the reliability of sources as well as the plausibility of statements about entities. In comparison to prior work our approach stands out (1) by allowing the incorporation of arbitrary features about sources and claims, (2) by generalizing from reliability per source towards a reliability function, and thus (3) enabling the estimation of source reliability even for sources that have provided no or very few claims, (4) by building on efficient and scalable stochastic inference algorithms, and (5) by outperforming the state-of-the-art by a considerable margin.