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In exploratory search tasks, alongside information retrieval, information representation is an important factor in sensemaking. In this paper, we explore a multi-layer extension to knowledge graphs, hierarchical knowledge graphs (HKGs), that combines hierarchical and network visualizations into a unified data representation asa tool to support exploratory search. We describe our algorithm to construct these visualizations, analyze interaction logs to quantitatively demonstrate performance parity with networks and performance advantages over hierarchies, and synthesize 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. Alongside the above study, we perform an additional mixed methods analysis of the effect of precision and recall on the performance of hierarchical knowledge graphs for two different exploratory search tasks. While the quantitative data shows a limited effect of precision and recall on user performance and user effort, qualitative data combined with post-hoc statistical analysis provides evidence that the type of exploratory search task (e.g., learning versus investigating) can be impacted by precision and recall. Furthermore, our qualitative analyses find that users are unable to perceive differences in the quality of extracted information. We discuss the implications of our results and analyze other factors that more significantly impact exploratory search performance in our experimental tasks.