Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance.

Position bias: The same item in different positions affects the user’s preferences, especially for items that are usually more difficult to see in the backward position. Standard double robust (DR) estimator in recommendation systems is widely used due to its accuracy and robust properties. However, the conventional DR estimator was doubtful of its highperformance variance. In this paper, we examined how to reduce the variance of the current DR estimator. Our main goal is to optimize the existing double robust estimation by the self-normalization method to reduce the variation. Our mathematical deduction shows that the self-normalized double robust estimation (SNDR) has less variance. The subsequent experimental steps demonstrate the low variance property of SNDR with both figure demonstration and data analysis.