Abstract:  Entity linking is widely applied in fields such as information mining and question-answering systems，playing a pivotal role in constructing knowledge graphs. However， it is noted that the majority of current entity linking methods inadequately leverage candidate entity information and merely implicitly consider the relationships between entities within the global model. In response， we propose an entity linking approach named TopDEL， which integrates topics and descriptive information. TopDEL leverages descriptive entity information to aid in the selection of words with significant relevance to the entities from their surrounding context. Concurrently， the BERTopic topic model is incorporated into the local model to extract topics from documents. The word distribution under each topic is then utilized to represent the relationships among various entities for entity linking purposes. Experimental results conducted on four publicly available datasets underscore the efficacy of the TopDEL method. 

Key words:  , entity linking； description information； topic； BERTopic； word distribution