“It answers questions that I didn’t know I had”: PhD students’ evaluation of an information-sharing knowledge graph

What is it about?

This study assesses a knowledge graph (KG) for interdisciplinary Ph.D. programs, addressing the problem of having information scattered across the web and challenges related to tacit knowledge exchange. The KG, constructed using Neo4J, and data from various sources, was evaluated by 15 Ph.D. students through participatory design workshops and interviews. Findings show the KG reduces uncertainty and aids decision-making, notably benefiting newcomers. Key features include visualizing student-faculty networks and accessing aggregated data. Concerns arose regarding crowdsourced data privacy. While participants desired more qualitative data, they acknowledged the KG's value in identifying relevant contacts in their community. This personalized approach fills a gap in existing approaches, offering scalable support for supporting interdisciplinary collaborations in an institution.

Featured Image