  Dialogue systems can leverage large pre-trained language models and knowledge
to generate fluent and informative responses. However, these models are still
prone to produce hallucinated responses not supported by the input source,
which greatly hinders their application. The heterogeneity between external
knowledge and dialogue context challenges representation learning and source
integration, and further contributes to unfaithfulness. To handle this
challenge and generate more faithful responses, this paper presents RHO
($\rho$) utilizing the representations of linked entities and relation
predicates from a knowledge graph (KG). We propose (1) local knowledge
grounding to combine textual embeddings with the corresponding KG embeddings;
and (2) global knowledge grounding to equip RHO with multi-hop reasoning
abilities via the attention mechanism. In addition, we devise a response
re-ranking technique based on walks over KG sub-graphs for better
conversational reasoning. Experimental results on OpenDialKG show that our
approach significantly outperforms state-of-the-art methods on both automatic
and human evaluation by a large margin, especially in hallucination reduction
(17.54% in FeQA).
