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In exploratory search tasks, alongside information retrieval, informationrepresentation is an important factor in sensemaking. In this paper, we explorea multi-layer extension to knowledge graphs, hierarchical knowledge graphs(HKGs), that combines hierarchical and network visualizations into a unifieddata representation asa tool to support exploratory search. We describe ouralgorithm to construct these visualizations, analyze interaction logs toquantitatively demonstrate performance parity with networks and performanceadvantages over hierarchies, and synthesize data from interaction logs,interviews, and thinkalouds on a testbed data set to demonstrate the utility ofthe unified hierarchy+network structure in our HKGs. Alongside the above study,we perform an additional mixed methods analysis of the effect of precision andrecall on the performance of hierarchical knowledge graphs for two differentexploratory search tasks. While the quantitative data shows a limited effect ofprecision and recall on user performance and user effort, qualitative datacombined with post-hoc statistical analysis provides evidence that the type ofexploratory search task (e.g., learning versus investigating) can be impactedby precision and recall. Furthermore, our qualitative analyses find that usersare unable to perceive differences in the quality of extracted information. Wediscuss the implications of our results and analyze other factors that moresignificantly impact exploratory search performance in our experimental tasks.

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