ERNIE is an enhanced language representation model that integrates knowledge graphs (KGs) with large-scale textual corpora to improve language understanding. The model leverages both lexical, syntactic, and knowledge information to enhance language representation. ERNIE outperforms BERT on knowledge-driven tasks and is comparable on common NLP tasks. The model uses a pre-training objective that incorporates knowledge by randomly masking entity alignments and predicting entities from KGs. ERNIE also employs masked language modeling and next sentence prediction, similar to BERT. The model architecture includes a textual encoder and a knowledgeable encoder that fuse token and entity information. ERNIE was evaluated on entity typing and relation classification tasks, achieving significant improvements over BERT. It also performed well on other NLP tasks. The results show that ERNIE effectively reduces the impact of noisy labels and improves language understanding by incorporating external knowledge. Future research directions include integrating knowledge into feature-based models and using diverse structured knowledge for language representation.ERNIE is an enhanced language representation model that integrates knowledge graphs (KGs) with large-scale textual corpora to improve language understanding. The model leverages both lexical, syntactic, and knowledge information to enhance language representation. ERNIE outperforms BERT on knowledge-driven tasks and is comparable on common NLP tasks. The model uses a pre-training objective that incorporates knowledge by randomly masking entity alignments and predicting entities from KGs. ERNIE also employs masked language modeling and next sentence prediction, similar to BERT. The model architecture includes a textual encoder and a knowledgeable encoder that fuse token and entity information. ERNIE was evaluated on entity typing and relation classification tasks, achieving significant improvements over BERT. It also performed well on other NLP tasks. The results show that ERNIE effectively reduces the impact of noisy labels and improves language understanding by incorporating external knowledge. Future research directions include integrating knowledge into feature-based models and using diverse structured knowledge for language representation.