SciKGDash: The Scientific Knowledge Graph Dashboard for Supporting Knowledge Curation

TL;DR

SciKGDash is a dashboard tool designed to support the hybrid curation of scientific knowledge graphs, improving efficiency and user experience in maintaining high-quality research knowledge bases.

Contribution

The paper introduces SciKGDash, a tailored dashboard for knowledge curation in RKGs, demonstrating its usability and potential for adaptation to other scientific knowledge graphs.

Findings

Successful completion of 4 out of 5 curation tasks in under 5 minutes

Positive user experience score (UEQ 1.93) for SciKGDash

Identified limitations in applying specific quality metrics across diverse RKGs

Abstract

Research knowledge graphs (RKGs) have emerged as essential technology for organizing scientific knowledge, but their success depends heavily on the quality of their underlying content. Knowledge curation is a critical task to ensure the quality of (research) knowledge graphs ((R)KGs), with human curation being the gold standard despite its time- and resource-intensive nature. Automated methods, while efficient, lack the precision of human expertise. Hybrid approaches, combining automated processes with human oversight, offer a promising solution to this challenge. Dashboards can act as supportive tools in hybrid curation approaches, offering real-time updates and visual overviews. This paper presents an action research study, conducted in collaboration with the Curation and Community Building (C&CB) team of the Open Research Knowledge Graph (ORKG), to explore the development of a dashboard, called SciKGDash, designed to support knowledge curation of the ORKG. SciKGDash serves as a minimum viable product (MVP) tailored to the needs of the C&CB team, with potential for adaptation to other (R)KGs. An experiment with 15 participants demonstrated the usability of SciKGDash, with successful completion of 4 out of 5 curation tasks in under 5 minutes. In addition, SciKGDash received a positive user experience rating (UEQ score of 1.93). While the tailored solution proved effective for the ORKG, the research also highlights limitations in applying specific quality metrics across diverse (R)KGs. Future work should focus on identifying common quality metrics and enhancing SciKGDash with user-friendly features for querying customized quality metrics. Overall, knowledge curation in RKGs remains an under-explored field, warranting further research.