  Passage re-ranking is to obtain a permutation over the candidate passage set
from retrieval stage. Re-rankers have been boomed by Pre-trained Language
Models (PLMs) due to their overwhelming advantages in natural language
understanding. However, existing PLM based re-rankers may easily suffer from
vocabulary mismatch and lack of domain specific knowledge. To alleviate these
problems, explicit knowledge contained in knowledge graph is carefully
introduced in our work. Specifically, we employ the existing knowledge graph
which is incomplete and noisy, and first apply it in passage re-ranking task.
To leverage a reliable knowledge, we propose a novel knowledge graph
distillation method and obtain a knowledge meta graph as the bridge between
query and passage. To align both kinds of embedding in the latent space, we
employ PLM as text encoder and graph neural network over knowledge meta graph
as knowledge encoder. Besides, a novel knowledge injector is designed for the
dynamic interaction between text and knowledge encoder. Experimental results
demonstrate the effectiveness of our method especially in queries requiring
in-depth domain knowledge.
