Search　engines　play　an　important　role　in　finding　useful　information　on　the　Web.　Many　of　the　popular　search　engines　are　text-based　and　unable　to　understand　the　semantics　of　the　posed　query.　Semantic　Web　promises　to　create　both　human　and　machine　understandable　data.　A　hybrid　web　search　method　which　combines　the　powers　of　Semantic　Web　and　traditional　keyword-matching　approaches　is　proposed　in　this　paper.　Ontology-based　analysis　of　query　keywords,　their　implicit　relationships,　and　corresponding　background　knowledge　are　incorporated　into　the　text-based　web　search　process.　The　resulting　search　query　provides　a　solution　which　improves　current　web　searching　results.　Based　on　the　Web　search　web　services　provided　by　Google,　a　prototype　of　meta-search　engine　which　can　intelligently　ease　and　guide　netizen＇　s　web　searching　efforts　is　initially　implemented.　©　2013　IEEE.