%0 Journal Article %A HUANG Jinjie %A ZHAO Xuanwei %A ZHANG Xinyao %A MA Jingping %A SHI Yuqi %T Short Text Entity Link Based on Domain Knowledge Graph %D 2022 %R 10.3778/j.issn.1002-8331.2007-0219 %J Computer Engineering and Applications %P 165-174 %V 58 %N 1 %X The task of entity linking is to identify potential entity references in the text and link it to unambiguous entity in a given knowledge base. In most cases, the entity link may contain the lack of effective contextual information in the Chinese short text, which leads to the ambiguity of polysemy. In the process of candidate linking, the uncertain correlation of candidate entities also affects the accuracy of candidate linking. Aiming at the above two problems, an entity link model based on the combination of deep neural network and association graph is proposed. The model adds character features, context, and deep semantics of information to enhance the representation of references and entities and performs similarity matching. The Fast-newman algorithm is used to divide the graph knowledge base into different types of entity clusters, and the entity clusters of the candidate entities with the highest similarity calculation scores are mapped to the relationship plane to construct the cluster entity association graph. The biased random walk algorithm examines the semantic relevance between candidate entities, calculates the matching degree between the reference and the candidate entity, and inputs linked entity. The model can realize the accurate link of short text to the target entity of the knowledge graph. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2007-0219