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Knowledge graph question answering aims to identify answers of the query according to the facts in the knowledge graph. In the vast majority of the existing works, the input queries are considered perfect and can precisely express the user’s query intention. However, in reality, input queries might be ambiguous and elusive which only contain a limited amount of information. Directly answering these ambiguous queries may yield unwanted answers and deteriorate user experience. In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. In order to leverage the hidden (pseudo) relevance information existed in the results that are initially returned from a given query, PReFNet treats the top-k returned candidate answers as a set of most relevant answers, and uses variational Bayesian inference to infer user’s query intention. To boost the quality of the inferred queries, a neighborhood embedding based VGAE model is used to prune inferior inferred queries. The inferred high quality queries will be returned to the users to help them search with ease. Moreover, all the high-quality candidate nodes will be re-ranked according to the inferred queries. The experiment results show that our proposed method can recommend high-quality query graphs to users and improve the question answering accuracy.