Text corpora have high coverage but extracting information from them is challenging whereas knowledge graphs are incomplete but are easier to extract answers from <cite>[17]</cite> . In this paper, we propose a relational GNN for open-domain question answering that learns contextual knowledge graph embeddings by jointly updating the embeddings from a knowledge graph and a set of linked documents.