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%252C+is+a+critical+task+in+knowledge+integration%252C+where+aligning+heterogeneous+ontologies+facilitates+data+interoperability+and+knowledge+sharing.+Traditional+OM+systems+often+rely+on+expert+knowledge+or+predictive+models%252C+with+limited+exploration+of+the+potential+of+Large+Language+Models+(LLMs).+We+present+the+LLMs4OM+framework%252C+a+novel+approach+to+evaluate+the+effectiveness+of+LLMs+in+OM+tasks.+This+framework+utilizes+two+modules+for+retrieval+and+matching%252C+respectively%252C+enhanced+by+zero-shot+prompting+across+three+ontology+representations%3a+concept%252C+concept-parent%252C+and+concept-children.+Through+comprehensive+evaluations+using+20+OM+datasets+from+various+domains%252C+we+demonstrate+that+LLMs%252C+under+the+LLMs4OM+framework%252C+can+match+and+even+surpass+the+performance+of+traditional+OM+systems%252C+particularly+in+complex+matching+scenarios.+Our+results+highlight+the+potential+of+LLMs+to+significantly+contribute+to+the+field+of+OM.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.fd6c5f3600c402b8894d7f30acab5b71%3frik%3dMwLthAqyUt6e7w%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Oyoom+Framework+Machine+Learning&FORM=IRPRST&ck=3BE06A10390A7A7963FEA2D1886D0E1D&selectedIndex=19&itb=0)
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