The LOD cloud offers a plethora of RDF data sources where users discover items of interest by issuing SPARQL queries. A common query problem for users is to face with empty answers: given a SPARQL query that returns nothing, how to refine the query to obtain a non-empty set? In this paper, we propose an RDF graph embedding based framework to solve the SPARQL empty-answer problem in terms of a continuous vector space. We first project the RDF graph into a continuous vector space by an entity context preserving translational embedding model which is specially designed for SPARQL queries. Then, given a SPARQL query that returns an empty set, we partition it into several parts and compute approximate answers by leveraging RDF embeddings and the translation mechanism. We also generate logical and alternative queries for returned answers, which helps users recognize their expectations and refine the original query finally. To validate the effectiveness and efficiency of our framework, we conduct extensive experiments on the real-world RDF dataset. The results show that our framework can significantly improve the quality of approximate answers and speed up the generation of alternative queries.