  Although the great success of open-domain dialogue generation, unseen
entities can have a large impact on the dialogue generation task. It leads to
performance degradation of the model in the dialog generation. Previous
researches used retrieved knowledge of seen entities as the auxiliary data to
enhance the representation of the model. Nevertheless, logical explanation of
unseen entities remains unexplored, such as possible co-occurrence or
semantically similar words of them and their entity category. In this work, we
propose an approach to address the challenge above. We construct a graph by
extracting entity nodes in them, enhancing the representation of the context of
the unseen entity with the entity's 1-hop surrounding nodes. Furthermore, We
added the named entity tag prediction task to apply the problem that the unseen
entity does not exist in the graph. We conduct our experiments on an open
dataset Wizard of Wikipedia and the empirical results indicate that our
approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.
