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%252C+fact+verification+still+remains+a+difficult+task.+Due+to+misinformation+spreading+increasingly+fast%252C+attention+has+been+directed+towards+automatically+verifying+the+correctness+of+claims.+In+the+domain+of+NLP%252C+this+is+usually+done+by+training+supervised+machine+learning+models+to+verify+claims+by+utilizing+evidence+from+trustworthy+corpora.+We+present+efficient+methods+for+verifying+claims+on+a+dataset+where+the+evidence+is+in+the+form+of+structured+knowledge+graphs.+We+use+the+FactKG+dataset%252C+which+is+constructed+from+the+DBpedia+knowledge+graph+extracted+from+Wikipedia.+By+simplifying+the+evidence+retrieval+process%252C+from+fine-tuned+language+models+to+simple+logical+retrievals%252C+we+are+able+to+construct+models+that+both+require+less+computational+resources+and+achieve+better+test-set+accuracy.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0e1a3fdba5816305b1c9dde2d3b33392%3frik%3dZ7zU97dLKIHD6w%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Fact+Cognition+Matrix+Example&simid=608025009239502987&FORM=IRPRST&ck=6438FA0447D53AE902C6E409E20B361D&selectedIndex=5&itb=0)
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