Community Design of a Knowledge Graph to Support Interdisciplinary PhD Students
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2023-03-14
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Abstract
How do PhD students discover the resources and relationships conducive to satisfaction and success in their degree programs? This study proposes a community-grounded, extensible knowledge graph to make explicit and tacit information intuitively discoverable, by capturing and visualizing relationships between people based on their activities and relations to information resources in a particular domain. Students in an interdisciplinary PhD program were engaged through three workshops to provide insights into the dynamics of interactions with others and relevant data categories to be included in the graph data model. Based on these insights we propose a model, serving as a testbed for exploring multiplex graph visualizations and a potential basis of the information system to facilitate information discovery and decision-making. We discovered that some of the tacit knowledge can be explicitly encoded, while the rest of it must stay within the community. The graph-based visualization of the social and knowledge networks can serve as a pointer toward the people having the relevant information, one can reach out to, online or in person.
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This is the submitted version of the paper, pre-revision.
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Gardasevic, S., & Gazan, R. (2023). Community Design of a Knowledge Graph to Support Interdisciplinary PhD Students. In I. Sserwanga, A. Goulding, H. Moulaison-Sandy, J. T. Du, A. L. Soares, V. Hessami, & R. D. Frank (Eds.), Information for a Better World: Normality, Virtuality, Physicality, Inclusivity (Vol. 13972, pp. 473–490). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-28032-0_36
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17
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Book chapter
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Hawaii
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