SmartReviews: Towards Human- and Machine-actionable Reviews

TL;DR

This paper introduces SmartReviews, a novel approach that leverages knowledge graphs to create semantic, machine-actionable review articles, aiming to improve the organization, readability, and impact of scholarly reviews.

Contribution

The paper proposes a new framework for review articles using knowledge graphs to enhance machine readability and community maintenance, addressing limitations of traditional reviews.

Findings

Knowledge graphs enable better organization of review content.

SmartReviews improve machine-actionability of scholarly reviews.

The approach facilitates community-driven maintenance of review articles.

Abstract

Review articles summarize state-of-the-art work and provide a means to organize the growing number of scholarly publications. However, the current review method and publication mechanisms hinder the impact review articles can potentially have. Among other limitations, reviews only provide a snapshot of the current literature and are generally not readable by machines. In this work, we identify the weaknesses of the current review method. Afterwards, we present the SmartReview approach addressing those weaknesses. The approach pushes towards semantic community-maintained review articles. At the core of our approach, knowledge graphs are employed to make articles more machine-actionable and maintainable.