Objective:　The　objective　of　this　study　was　to　propose　a　graph-based　semantic　search　approach　by　addressing　the　inherent　complexity　and　ambiguity　of　medical　terminology　in　queries　and　clinical　text　for　enhanced　medical　information　retrieval.　Methods:　The　supportive　use　of　a　medical　domain　ontology　exploits　the　light-weight　semantics　discovered　from　queries　and　documents　for　enhanced　document　ranking.　First,　the　implicit　information　regarding　concepts　and　the　relations　between　them　is　discovered　in　the　documents　and　queries　and　is　used　to　evaluate　the　relevance　of　the　query-document;　then,　the　semantic　linkages　between　concepts　distributed　in　target　documents　and　reference　documents　are　built　and　used　to　score　the　document＇s　popularity;　finally,　the　above　two　evaluations　are　integrated　to　produce　the　final　ranking　list　for　document　ranking.　Results:　Empirical　experiments　are　conducted　on　two　different　datasets.　The　results　demonstrate　that　the　proposed　graph-based　approach　significantly　outperforms　the　baselines.　For　example,　the　average　performance　improvement　on　two　datasets　of　the　best　variant　of　GSRM　compared　to　the　best　baseline　achieve　7.2%　and　7.9%　in　terms　of　P@20　and　NDCG@20,　respectively,　which　illustrates　the　effectiveness　of　the　proposed　approach.