  The conversational search paradigm introduces a step change over the
traditional search paradigm by allowing users to interact with search agents in
a multi-turn and natural fashion. The conversation flows naturally and is
usually centered around a target field of knowledge. In this work, we propose a
knowledge-driven answer generation approach for open-domain conversational
search, where a conversation-wide entities' knowledge graph is used to bias
search-answer generation. First, a conversation-specific knowledge graph is
extracted from the top passages retrieved with a Transformer-based re-ranker.
The entities knowledge-graph is then used to bias a search-answer generator
Transformer towards information rich and concise answers. This conversation
specific bias is computed by identifying the most relevant passages according
to the most salient entities of that particular conversation. Experiments show
that the proposed approach successfully exploits entities knowledge along the
conversation, and outperforms a set of baselines on the search-answer
generation task.
