In　the　era　of　Internet　and　big　data,　an　increasing　number　of　intelligent　applications　have　been　developed.　As　the　result,　a　lot　of　user　data　can　be　collected　and　stored　by　Internet　companies　as　well　as　by　ordinary　users　through　various　media　platforms　such　as　Facebook,　WeChat,　etc.　that　may　contain　information　related　to　personal　privacy.　Even　though　privacy　protection　has　been　declared　by　Internet　service　providers,　after　collecting　enough　amount　of　seemingly　less　relevant　data,　an　attacker　can　still　infer　user　privacy　via　one　means　or　another,　e.g.,　by　running　a　data　mining　algorithm.　This　can　undoubtedly　bring　high　risk　of　privacy　disclosure　to　users　under　such　an　attack　model.　So,　accurately　measuring　the　leakage　of　privacy　becomes　an　urgent　issue.　Although　many　privacy　measurement　and　protection　methods　have　been　proposed　in　recent　years,　they　mainly　target　at　structured　datasets　and　are　thus　inadequate　to　the　measurement　of　the　disclosure　of　specific　privacy　information.　In　addition,　most　of　the　methods　have　failed　to　consider　the　internal　connections　and　relationships　between　privacy　information　and　thus　cannot　be　used　to　measure　the　implicit　privacy　disclosure　risk　on　unstructured　data.　In　this　paper,　we　propose　a　semantic　inference　method　based　on　the　WordNet　ontology　to　measure　privacy　disclosure　in　which　we　employ　an　information　content　(IC)　based　method　to　determine　the　weight　of　attributes　to　describe　the　inference　preferences　in　the　process　of　inferring　privacy.　Experiment　was　performed　to　verify　the　effectiveness　of　the　IC　based　inference　weight　assignment　method　and　to　compare　the　proposed　measurement　method　to　some　privacy　disclosure　behavior　learned　through　a　data　mining　algorithm　and　some　existing　privacy　measurement　methods　to　demonstrate　the　advantages　of　the　proposed　method　for　measuring　privacy　disclosure.