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Context: Knowledge propagation via meta-graph.It is worth noting that, the above-defined knowledge injection process only leverages knowledge embeddings learned by TransE on the global graph \overline{\mathcal{G}}. Particularly, it lacks considering the knowledge that bridges the semantics between query and passage. To this end, we introduce a Graph Meta Network (GMN) module that refines knowledge with the constructed meta-graph \mathbf{G}_{\mathbf{q},\mathbf{p}}, The multi-hop paths of \mathbf{G}_{\mathbf{q},\mathbf{p}} allow the knowledge to be propagated between query and passage, which can enhance the relevance signal to be captured by the model, and thus alleviate the semantic gap. For knowledge aggregation, we design a novel interaction module between text and knowledge graph to combine the implicit and explicit knowledge. To derive implicit knowledge from text, we employ PLM as text encoder. To be aligned with implicit knowledge, knowledge meta graph is encoded with a multi-layer graph neural network (i.e. k-hop), namely Graph Meta Network (GMN). Each transformer layer outputs word representations. Each graph meta network layer outputs entity representations. Both word and entity representations are aggregated as the input of the following transformer and GMN layer, respectively in a novelly designed module, namely knowledge injector. Therefore through knowledge aggregation, implicit knowledge from text corpus and explicit knowledge from existing knowledge graph can mutually boost each other to achieve a better re-ranking performance, in which the issues in Challenge 2. could be mitigated. Overall, our contributions can be summarized as follows:•It is the first attempt to solve the knowledge enhanced PLMs problem for passage re-ranking. The key motivation lies in that bridging the semantic gap between the query and passage with the help of both kinds of knowledge.•We design a novel knowledge graph distillation method. It refines a reliable knowledge graph from the existing one globally and constructs a knowledge meta graph based on the refined graph locally.•We propose a novel aggregation of PLM and graph neural network framework to model the interaction between explicit knowledge and implicit knowledge.•Experimental results show the effectiveness of KERM on both general and domain specific data, achieving state-of-the-art performance for passage re-ranking. We also conduct a comprehensive study for the effects of each module in our method. The code is available at https://github.com/DQ0408 /KERM.

Question: What components of the proposed method aggregate explicit knowledge into implicit knowledge for query and passage embedding?
This work proposes an aggregation module that employs a PLM and a Graph Neural Network (GMN) to model the interaction between explicit and implicit knowledge. The PLM encodes text to obtain word representations (i.e., implicit knowledge), and the Graph Neural Network (GMN) encodes knowledge meta-graphs to obtain entity representations (i.e., explicit knowledge). This module aggregates the word and entity representations to aggregate the implicit and explicit knowledge.