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Biomedical document retrieval requires entity level processing instead of term level. This paper explores the usage and impact of UMLS for entity-based query reformulation in biomedical document retrieval. A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. The proposed method considers UMLS entities from a query with their related entities identified by UMLS and constructs a query-specific graph of biomedical entities for term selection. This query reformulation approach is compared with baseline, pseudo relevance feedback based query expansion approach and state-of-the-art UMLS based query reformulation approaches. The experiments on CDS 2015 and CDS 2016 datasets shows 35% and 45% improvement in retrieval performance, respectively., (Copyright © 2020 Elsevier Inc. All rights reserved.)