Nowadays,　with　the　continual　development　of　digital　capture　technologies　and　social　media　services,　a　vast　number　of　media　documents　are　captured　and　shared　online　to　help　attendees　record　their　experience　during　events.　In　this　paper,　we　present　a　method　combining　semantic　inference　and　multimodal　analysis　for　automatically　finding　media　content　to　illustrate　events　using　an　adaptive　probabilistic　hypergraph　model.　In　this　model,　media　items　are　taken　as　vertices　in　the　weighted　hypergraph　and　the　task　of　enriching　media　to　illustrate　events　is　formulated　as　a　ranking　problem.　In　our　method,　each　hyperedge　is　constructed　using　the　K-nearest　neighbors　of　a　given　media　document.　We　also　employ　a　probabilistic　representation,　which　assigns　each　vertex　to　a　hyperedge　in　a　probabilistic　way,　to　further　exploit　the　correlation　among　media　data.　Furthermore,　we　optimize　the　hypergraph　weights　in　a　regularization　framework,　which　is　solved　as　a　second-order　cone　problem.　The　approach　is　initiated　by　seed　media　and　then　used　to　rank　the　media　documents　using　a　transductive　inference　process.　The　results　obtained　from　validating　the　approach　on　an　event　dataset　collected　from　EventMedia　demonstrate　the　effectiveness　of　the　proposed　approach.