Designing Usable Knowledge Graphs: The Case of an Interdisciplinary PhD Program

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.