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+aim+at+solving+queries+like+(h%252C+r%252C+%253F)+in+different+languages+by+reasoning+a+tail+entity+t+thus+improving+multilingual+knowledge+graphs.+Previous+studies+leverage+multilingual+pretrained+language+models+(PLMs)+and+the+generative+paradigm+to+achieve+mKGC.+Although+multilingual+pretrained+language+models+contain+extensive+knowledge+of+different+languages%252C+its+pretraining+tasks+cannot+be+directly+aligned+with+the+mKGC+tasks.+Moreover%252C+the+majority+of+KGs+and+PLMs+currently+available+exhibit+a+pronounced+English-centric+bias.+This+makes+it+difficult+for+mKGC+to+achieve+good+results%252C+particularly+in+the+context+of+low-resource+languages.+To+overcome+previous+problems%252C+this+paper+introduces+global+and+local+knowledge+constraints+for+mKGC.+The+former+is+used+to+constrain+the+reasoning+of+answer+entities%252C+while+the+latter+is+used+to+enhance+the+representation+of+query+contexts.+The+proposed+method+makes+the+pretrained+model+better+adapt+to+the+mKGC+task.+Experimental+results+on+public+datasets+demonstrate+that+our+method+outperforms+the+previous+SOTA+on+Hits%25401+and+Hits%254010+by+an+average+of+12.32%2525+and+16.03%2525%252C+which+indicates+that+our+proposed+method+has+significant+enhancement+on+mKGC.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2be2d7c338a112fc9c37995257771699%3frik%3d8N2c17HUFPlozA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multilingual+Knowledge+Graph&FORM=IRPRST&ck=6E07832B07ED6716CCE4DF7FC88CFCD3&selectedIndex=23&itb=0)
