The　advantages　offered　by　the　presence　of　a　schema　are　numerous.　However,　many　XML　documents　in　practice　are　not　accompanied　by　a　(valid)　schema,　making　schema　inference　an　attractive　research　problem.　The　fundamental　task　in　XML　schema　learning　is　inferring　restricted　subclasses　of　regular　expressions.　Most　previous　work　either　lacks　support　for　interleaving　or　only　has　limited　support　for　interleaving.　In　this　paper,　we　first　propose　a　new　subclass　Single　Occurrence　Regular　Expressions　with　Interleaving　(SOIRE),　which　has　unrestricted　support　for　interleaving.　Then,　based　on　single　occurrence　automaton　and　maximum　independent　set,　we　propose　an　algorithm　SOIRE　to　infer　SOIREs.　Finally,　we　further　conduct　a　series　of　experiments　on　real　datasets　to　evaluate　the　effectiveness　of　our　work,　comparing　with　both　ongoing　learning　algorithms　in　academia　and　industrial　tools　in　real-world.　The　results　reveal　the　practicability　of　SOIRE　and　the　effectiveness　of　SOIRE,　showing　the　high　preciseness　and　conciseness　of　our　work.　©　2019　Association　for　Computing　Machinery.