Human-in-the-Loop Workflow for Systematic Creation of Content for Scientific Knowledge Graphs

Abstract

As the volume of scientific literature continues to grow, efficient knowledge curation is becoming an increasingly challenging task. Traditional manual processes for structuring scientific content are time-consuming and require significant domain expertise, increasing the need for tool support. Our goal is to create a Human-in-the-Loop workflow that supports researchers in creating and structuring scientific knowledge for the integration into knowledge graphs, exemplary the Open Research Knowledge Graph (ORKG) in this paper. The workflow aims to automate key steps, including data extraction and knowledge structuring, while keeping user oversight through human validation points. A tool implementing the workflow is developed and evaluated along the Quality Improvement Paradigm (QIP) with 9 participants from the ORKG domain. The evaluation demonstrated that the tool offers practical support for the ORKG users, by significantly reducing the time required to transform a research interest into a structured knowledge graph representation. Furthermore, participants reported positive feedback regarding the usability of the tool. However, further work is needed to enhance the quality of the extracted data and provide more accurate entity linking for pre-existing resources