Knowledge　graphs　(KGs)　have　found　extensive　applications　within　intelligent　systems,　such　as　information　retrieval.　Much　of　the　research　has　predominantly　focused　on　completing　missing　knowledge,　with　little　consideration　given　to　examining　errors.　Unfortunately,　during　customizing　KGs,　diverse　unpredictable　errors　are　virtually　unavoidable　to　be　introduced,　and　these　anomalies　significantly　impact　the　performance　of　applications.　Detecting　erroneous　knowledge　presents　a　formidable　challenge　due　to　the　costly　acquisition　of　ground　-truth　labels.　In　this　work,　we　develop　an　unsupervised　anomaly　detection　framework　named　ProMvSD,　aiming　to　adapt　KGs　of　varying　scales　via　serialization　components.　To　overcome　the　insufficient　contextual　information　provided　by　the　topological　structure,　we　introduce　the　large　language　model　as　a　reasoner　to　extract　prior　knowledge　from　extensive　pre　-trained　textual　data,　thereby　enhancing　the　understanding　of　KGs.　Anomalous　triple　may　result　in　a　larger　semantic　gap　between　the　head　and　tail　neighborhoods.　To　uncover　latent　anomalies　effectively,　we　propose　a　multi　-view　semantic　-driven　model　(MvSD)　based　on　the　assumptions　of　self　-consistency　and　information　stability.　MvSD　jointly　estimates　the　suspiciousness　of　triples　from　three　hyperviews:　node　-view　semantic　contradiction,　triple　-view　semantic　gap,　and　pathway　-view　semantic　gap.　Extensive　experiments　on　three　English　benchmark　KGs　and　a　Chinese　medical　KG　demonstrate　that,　for　the　top　1%　of　the　most　suspicious　triples,　we　can　detect　real　anomalies　with　at　most　99.9%　accuracy.　Furthermore,　ProMvSD　significantly　outperforms　state-of-the-art　representation　learning　baselines,　achieving　a　29.2%　improvement　in　detecting　all　anomalies.