In　this　paper,　we　investigate　the　empirical　likelihood　for　constructing　a　confidence　region　of　the　parameter　of　interest　in　a　multi-link　semiparametric　model　when　an　infinite-dimensional　nuisance　parameter　exists.　The　new　model　covers　the　commonly　used　varying　coefficient,　generalized　linear,　single-index,　multi-index,　hazard　regression　models　and　their　generalizations,　as　its　special　cases.　Because　of　the　existence　of　the　infinite-dimensional　nuisance　parameter,　the　classical　empirical　likelihood　with　plug-in　estimation　cannot　be　asymptotically　distribution-free,　and　the　existing　bias　correction　is　not　extendable　to　handle　Such　a　general　model.　We　then　propose　a　link-based　correction　approach　to　solve　this　problem.　This　approach　gives　a　general　rule　of　bias　correction　via　an　inner　link,　and　consists　of　two　parts.　For　the　model　whose　estimating　equation　contains　the　score　functions　that　are　easy　to　estimate,　we　use　a　centering　for　the　scores　to　correct　the　bias;　for　the　model　of　which　the　score　functions　are　of　complex　structure,　a　bias-correction　procedure　using　simpler　functions　instead　of　the　scores　is　given　without　loss　of　asymptotic　efficiency.　The　resulting　empirical　likelihood　shares　the　desired　features:　it　has　a　chi-square　limit　and,　under-smoothing　technique,　high　order　kernel　and　parameter　estimation　are　not　needed.　Simulation　studies　are　carried　out　to　examine　the　performance　of　the　new　method.　(C)　2009　Elsevier　Inc.　All　rights　reserved.