The construction and updating of the knowledge graph(KG)usually depend on a wide range of web data and automated methods,inevitably resulting in factual inaccuracies in the modeled and acquired knowledge.To tackle this problem,a novelap-proach for identifying factual inaccuracies within the knowledge graph is proposed.This method actively selects adjacent nodes of the facts to be checked,detecting errors by measuring the intricate associations linking the head and tail entities.More specifically,it first utilizes graph structure information to identify potential neighbors for each entity.Then,based on contextual information,it dy-namically selects relevant neighbors and uses an efficient graph attention network to encode node features.Finally,by calculating the consistency of head and tail entity representations,it determines if the fact under consideration is erroneous.Experimental results on multiple public KG datasets demonstrate that this method outperforms existing approaches in error detection.
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