Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation

TL;DR

Research Knowledge Graphs (RKGs) offer a new, structured way to represent and connect research artifacts, addressing challenges of heterogeneity and unstructured data in scholarly communication.

Contribution

This paper conceptualizes the RKG vision, categorizes existing RKGs, and analyzes their building blocks, principles, and construction methodologies.

Findings

Survey of diverse real-world RKG implementations

Identification of core RKG building blocks and principles

Discussion of future applications and challenges

Abstract

Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured publications pose crucial challenges. Reproducibility of research and finding state-of-the-art methods or data have become increasingly challenging. In this context, the concept of Research Knowledge Graphs (RKGs) has emerged, aiming at providing an easy to use and machine-actionable representation of research artifacts and their relations. That is facilitated through the use of established principles for data representation, the consistent adoption of globally unique persistent identifiers and the reuse and linking of vocabularies and data. This paper provides the first conceptualisation of the RKG vision, a categorisation of in-use RKGs together with a description of RKG building blocks and principles. We also survey real-world RKG implementations differing with respect to scale, schema, data, used vocabulary, and reliability of the contained data. We also characterise different RKG construction methodologies and provide a forward-looking perspective on the diverse applications, opportunities, and challenges associated with the RKG vision.