The　advent　of　smart　manufacturing　in　Industry　4.0　signifies　the　era　of　connections.　As　a　communication　protocol,　Object　linking　and　embedding　for　Process　Control　Unified　Architecture　(OPC　UA)　can　address　most　semantic　heterogeneity　issues.　However,　its　semantics　are　not　formally　defined　at　the　application　layer.　To　address　the　information　silo　problem　caused　by　semantic　heterogeneity,　an　integration　framework　named　Querying　of　Ontology　Mapping-based　OPC　UA　(QOMOU)　is　proposed.　QOMOU　extracts　information　models　of　OPC　UA　servers　into　resource　description　framework　triples　and　utilizes　web　ontology　language　for　semantic　enrichment　and　inference.　Then,　an　Event　Class　Semantic　Similarity　Calculation　(ECSSC)　method　is　proposed　for　device　type　identification,　enabling　the　classification　of　semantically　heterogeneous　OPC　UA　devices.　The　effectiveness　of　ECSSC　is　validated　through　queries　with　the　RDF　query　language　(SPARQL)　protocol　in　Apache　Jena.　Experimental　results　demonstrate　that　ECSSC　improves　the　accuracy　of　device　identification　by　approximately　7%　compared　to　benchmark　device　identification　models.　Specifically,　compared　with　graph　embedding-based　methods,　QOMOU’s　query　performance　is　approximately　13%　higher,　and　its　query　efficiency　is　5%　higher　on　average　compared　to　both　structured　query　and　extensible　markup　languages.　Moreover,　by　employing　a　keyword-matching　algorithm,　the　query　accuracy　of　the　existing　heterogeneous　data　integration　scheme　is　improved　by　4%　on　average.　This　enhancement　can　boost　the　operational　efficiency　of　Internet　of　Things　systems　based　on　the　OPC　UA　architecture.　©　2014　IEEE.