Bahareh Sarrafzadeh, PhD candidate
David R. Cheriton School of Computer Science
In information retrieval and information visualization, hierarchies are a common tool to structure information into topics or facets, and network visualizations such as knowledge graphs link related concepts within a domain. In this talk, we explore a multi-layer extension to knowledge graphs, hierarchical knowledge graphs (HKGs), that combines hierarchical and network visualizations into a unified data representation. Through interaction logs, we show that HKGs preserve the benefits of single-layer knowledge graphs at conveying domain knowledge while incorporating the sense-making advantages of hierarchies for knowledge seeking tasks.
Specially, this talk describes our algorithm to construct these visualizations, analyzes interaction logs to quantitatively demonstrate performance parity with networks and performance advantages over hierarchies, and synthesizes data from interaction logs, interviews, and thinkalouds on a testbed data set to demonstrate the utility of the unified hierarchy + network structure in our HKGs.
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Waterloo, ON N2L 3G1