Disambiguation method of name entities embedded in meta-path heterogeneous networks

In order to solve the problem of disambiguation of duplicate authors in large academic databases, a name entity disambiguation model based on meta-path heterogeneous network was proposed. Based on the public data of the large online academic search system DBLP, the author information, title, name of conference journal and other characteristic attributes of academic publications were extracted first. Then the characteristic attribute words generated by the word2vec model tool were embedded into the GRU network for training, so that a PHNet matrix network for random walk operation was constructed to capture the relationship between different types of nodes and finally similar nodes were divided to complete the name disambiguation. The experimental results show that the accuracy of the method is 0.865, the recall rate is 0.792, and the F1 value is 0.815.The meta-path-based heterogeneous network embedding model is superior to the comparison model in terms of accuracy and recall rate. Therefore, the proposed model has a good application prospect in improving the accuracy of disambiguation of large academic databases.