Aiming at the problems of too many long-tail entities and existing knowledge and the scarcity of labeled data in the current entity alignment tasks in the tourism domain, this paper proposed an entity alignment method based on the combination of multi-view knowledge representation and neural network. It used a pre-trained model to complete the knowledge representation learning of multiple views, obtained the structural embedding, relationship embedding and description information embedding of the entity, and then used the convolutional neural network to calculate the similarity of the comprehensive embedding of the entities combined with the three views. The experimental accuracy rate reached 91.4%, the recall rate reached 87.9%, and the comprehensive index F₁value reached 89.6%. The results show that the method effectively completes the entity alignment task in the tourism domain.

[2022-11-08] Accepted Paper /doc/2022.08.0434/v1

[2023-04-05] Printed Article /doc/2022.08.0434/v2