In a large number of applications, from biomedical literature to social networks, there are collections of text documents that are annotated by interconnected entities, which are related to each other through association graphs. For example, social posts are related through the friendship graph of their authors, and PubMed articles area annotated by Mesh terms, which are related through ontological relationships. To effectively query such collections, in addition to the text content relevance of a document, the semantic distance between the entities of a document and the query must be taken into account. In this paper, we propose a novel query framework, which we refer as keyword querying on graph-annotated documents, and query techniques to answer such queries. Our methods automatically balance the impact of the graph entities and the text content in the ranking. Our qualitative evaluation on real dataset shows that our methods improve the ranking quality compared to baseline ranking systems.