  Exploratory search is an open-ended information retrieval process that aims
at discovering knowledge about a topic or domain rather than searching for a
specific answer or piece of information. Conversational interfaces are
particularly suitable for supporting exploratory search, allowing users to
refine queries and examine search results through interactive dialogues. In
addition to conversational search interfaces, knowledge graphs are also useful
in supporting information exploration due to their rich semantic representation
of data items. In this study, we demonstrate the synergistic effects of
combining knowledge graphs and conversational interfaces for exploratory
search, bridging the gap between structured and unstructured information
retrieval. To this end, we propose a knowledge-driven dialogue system for
exploring news articles by asking natural language questions and using the
graph structure to navigate between related topics. Based on a user study with
54 participants, we empirically evaluate the effectiveness of the graph-based
exploratory search and discuss design implications for developing such systems.
