Multilingual Knowledge Graph
%20aim%20at%20solving%20queries%20like%20(h%2C%20r%2C%20%3F)%20in%20different%20languages%20by%20reasoning%20a%20tail%20entity%20t%20thus%20improving%20multilingual%20knowledge%20graphs.%20Previous%20studies%20leverage%20multilingual%20pretrained%20language%20models%20(PLMs)%20and%20the%20generative%20paradigm%20to%20achieve%20mKGC.%20Although%20multilingual%20pretrained%20language%20models%20contain%20extensive%20knowledge%20of%20different%20languages%2C%20its%20pretraining%20tasks%20cannot%20be%20directly%20aligned%20with%20the%20mKGC%20tasks.%20Moreover%2C%20the%20majority%20of%20KGs%20and%20PLMs%20currently%20available%20exhibit%20a%20pronounced%20English-centric%20bias.%20This%20makes%20it%20difficult%20for%20mKGC%20to%20achieve%20good%20results%2C%20particularly%20in%20the%20context%20of%20low-resource%20languages.%20To%20overcome%20previous%20problems%2C%20this%20paper%20introduces%20global%20and%20local%20knowledge%20constraints%20for%20mKGC.%20The%20former%20is%20used%20to%20constrain%20the%20reasoning%20of%20answer%20entities%2C%20while%20the%20latter%20is%20used%20to%20enhance%20the%20representation%20of%20query%20contexts.%20The%20proposed%20method%20makes%20the%20pretrained%20model%20better%20adapt%20to%20the%20mKGC%20task.%20Experimental%20results%20on%20public%20datasets%20demonstrate%20that%20our%20method%20outperforms%20the%20previous%20SOTA%20on%20Hits%401%20and%20Hits%4010%20by%20an%20average%20of%2012.32%25%20and%2016.03%25%2C%20which%20indicates%20that%20our%20proposed%20method%20has%20significant%20enhancement%20on%20mKGC.)
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