Hierarchical Knowledge Graphs: A Novel Information Representation for Exploratory Search Tasks

In exploratory search tasks, alongside information retrieval, information\nrepresentation is an important factor in sensemaking. In this paper, we explore\na multi-layer extension to knowledge graphs, hierarchical knowledge graphs\n(HKGs), that combines hierarchical and network visualizations into a unified\ndata representation asa tool to support exploratory search. We describe our\nalgorithm to construct these visualizations, analyze interaction logs to\nquantitatively demonstrate performance parity with networks and performance\nadvantages over hierarchies, and synthesize data from interaction logs,\ninterviews, and thinkalouds on a testbed data set to demonstrate the utility of\nthe unified hierarchy+network structure in our HKGs. Alongside the above study,\nwe perform an additional mixed methods analysis of the effect of precision and\nrecall on the performance of hierarchical knowledge graphs for two different\nexploratory search tasks. While the quantitative data shows a limited effect of\nprecision and recall on user performance and user effort, qualitative data\ncombined with post-hoc statistical analysis provides evidence that the type of\nexploratory search task (e.g., learning versus investigating) can be impacted\nby precision and recall. Furthermore, our qualitative analyses find that users\nare unable to perceive differences in the quality of extracted information. We\ndiscuss the implications of our results and analyze other factors that more\nsignificantly impact exploratory search performance in our experimental tasks.\n