Research on Confusing Entity Linking Method Based on Graph Neural Network

Entity Linking EL is a fundamental task in Natural Language Processing NLP , aiming to accurately map entity mentions in text to their corresponding entities in a knowledge base, thereby bridging unstructured text with structured knowledge. EL plays a crucial role in various applications, including infor-mation retrieval, knowledge graph construction, and question answering. With the rapid advancement of deep learning, entity linking methods based on pre-trained language models PLMs have made remarkable progress, particularly in terms of semantic representation and context understanding. However, these methods still face challenges when dealing with ambiguous candidate entities, such as homonyms, synonyms, or cases with insufficient contextual infor-mation, which often lead to incorrect disambiguation. To address this issue, this paper proposes Confusing Entity Linking model based on Graph Neural Networks CEL-GNN , which leverages graph structures to capture subtle dif-ferences between candidate entity descriptions, thereby enhancing the accura-cy and robustness of entity linking. The proposed model first employs a BERT-based encoding layer to generate representations for both short texts and candidate entity descriptions. It then applies the TF-IDF method to extract keywords and construct a knowledge graph. Subsequently, a Graph Distillation Operator GDO is introduced to extract distinguishable features, further im-proving the disambiguation performance. Experimental results demonstrate that the proposed approach achieves outstanding performance on the CCKS2020 Chinese short-text entity linking benchmark. Compared to the baseline BERT model, our method achieves an F1 score of 88.9, significantly improving entity linking effectiveness.