Designing Usable Knowledge Graphs: The Case of an Interdisciplinary PhD Program
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2023
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Abstract
This study applies human-centered design principles and involves end users in creating a usableknowledge graph to explore the potential advantages of capturing and presenting networked
knowledge in a domain for information discovery and decision-making. The case taken for the
research is the interdisciplinary Ph.D. program in Communication and Information Sciences
(CIS) at the University of Hawaiʻi at Mānoa, and the Ph.D. students are the main user population
for the knowledge-graph-based information system. The graph was designed to help this user
population successfully progress through the degree by supporting them in information
discovery, decision-making, and tacit knowledge exchange within the community. The study was
conducted in three stages (i) requirements gathering, (ii) design of graph and visualizations (iii)
evaluation of the graph, and these stages are presented through three papers. CIS Ph.D. students
were involved in all study stages, and the methods applied were: semi-structured interviews,
website usability study, workshops, surveys, and content analysis. One of the outputs of this
study is a comprehensive CIS Knowledge Graph model and dataset that aggregates data from (i)
multiple academic websites, (ii) metadata from publications and dissertations relevant to this
community, and (iii) crowdsourced student data, making it a rich, multilayered network. Upon
evaluating the utility of this dataset with CIS Ph.D. students in different stages of the program,
the results show that having such a dataset available can be beneficial, especially for new
students, as this approach saves them time and effort when looking for relevant information;
helps them anticipate future steps and compare their progress with other students; and supports
them in making data-driven decisions–especially concerning choosing research supervisor and
collaborators–based on elicited statistics and the experiences of their peers and previous students.
This study contributes to our understanding of the information needs of interdisciplinary Ph.D.
students, showing that most of them rely on information they get from other community
members. It produced the knowledge graph model that can be repurposed for other
interdisciplinary research settings and a unique multilayered network dataset with pertinent use
scenarios/queries that can serve as a testbed for designing graph visualizations. Finally, based on
the end-user inputs and interactions with the knowledge graph hosted and demonstrated via the
Neo4J graph database tool, the study produces the guidelines for designing information
system that will host such a graph and will support the end users’ needs and requirements, with
the emphasis on the tacit knowledge exchange.
Description
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Information science, Communication, Education, Decision Making, Human-Centered Design, Interdisciplinary Research, Knowledge Graphs, PhD Students, Tacit Knowledge Exchange
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220 pages
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