  Scientists always look for the most accurate and relevant answer to their
queries on the scholarly literature. Traditional scholarly search systems list
documents instead of providing direct answers to the search queries. As data in
knowledge graphs are not acquainted semantically, they are not
machine-readable. Therefore, a search on scholarly knowledge graphs ends up in
a full-text search, not a search in the content of scholarly literature. In
this demo, we present a faceted search system that retrieves data from a
scholarly knowledge graph, which can be compared and filtered to better satisfy
user information needs. Our practice's novelty is that we use dynamic facets,
which means facets are not fixed and will change according to the content of a
comparison.
