Title: A Retrieve-and-Read Framework for Knowledge Graph Reasoning. Abstract: Knowledge graph (KG) reasoning aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG reasoning follow the standard message-passing paradigm on the entire KG, which leads to over-smoothing of representations and also limits their scalability. At a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address limitations of existing KG reasoning frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention from deep fusing between query and context. This design enables the model to focus on salient subgraph information that is relevant to the query. Empirical experiments on two standard KG reasoning datasets demonstrate the competitive performance of the proposed method.