  Conversational search has evolved as a new information retrieval paradigm,
marking a shift from traditional search systems towards interactive dialogues
with intelligent search agents. This change especially affects exploratory
information-seeking contexts, where conversational search systems can guide the
discovery of unfamiliar domains. In these scenarios, users find it often
difficult to express their information goals due to insufficient background
knowledge. Conversational interfaces can provide assistance by eliciting
information needs and narrowing down the search space. However, due to the
complexity of information-seeking behavior, the design of conversational
interfaces for retrieving information remains a great challenge. Although prior
work has employed user studies to empirically ground the system design, most
existing studies are limited to well-defined search tasks or known domains,
thus being less exploratory in nature. Therefore, we conducted a laboratory
study to investigate open-ended search behavior for navigation through unknown
information landscapes. The study comprised of 26 participants who were
restricted in their search to a text chat interface. Based on the collected
dialogue transcripts, we applied statistical analyses and process mining
techniques to uncover general information-seeking patterns across five
different domains. We not only identify core dialogue acts and their
interrelations that enable users to discover domain knowledge, but also derive
design suggestions for conversational search systems.
