KGMM -- A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration

TL;DR

This paper introduces KGMM, a maturity model designed to evaluate and guide the development of scholarly knowledge graphs through intertwined human-machine collaboration, emphasizing quality and evolutionary curation.

Contribution

It presents a novel five-stage maturity model with 20 quality measures tailored for collaborative scholarly knowledge graph curation.

Findings

Implemented in a large-scale scholarly knowledge graph project

Provides a structured framework for assessing knowledge graph maturity

Enhances understanding of quality aspects in human-machine collaborative curation

Abstract

Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the specific aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which specifically focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation effort.