Abstract: Entity Linking is an important research content in the field of Knowledge Graph. Most of the existing entity linking models focus on the selection of manual features, which cannot make good use of the semantic information between entities to achieve better efficient entity linking effect. Therefore, an improved entity linking model based on deep structured semantic model and convolutional neural network is proposed. It captures deep semantic information and extracts features through CNN, and uses them as input of the deep structured semantic model. It selects the best parameter through model training, and outputs candidate entities with the highest semantic similarity as the result of entity linking. Compared with the ranking SVM model, the proposed model improves the accuracy by 3.9% to 86.7% on the NLP & CC2014_ERL dataset. The experimental results show that the proposed model is effective and superior to the current mainstream model in entity linking tasks.

Key words: entity linking, knowledge graph, convolutional neural network, deep structured semantic model, semantic similarity