An integrated pipeline model for biomedical entity alignment

Biomedical entity alignment, composed of two sub-tasks: entity identification and entity-concept mapping, is of great research value in biomedical text mining while these techniques are widely used for name entity standardization, information retrieval, knowledge acquisition and ontology construction. Previous works made many efforts on feature engineering to employ feature-based models for entity identification and alignment. However, the models depended on subjective feature selection may suffer error propagation and are not able to utilize the hidden information. With rapid development in health-related research, researchers need an effective method to explore the large amount of available biomedical literatures. Therefore, we propose a two-stage entity alignment process, biomedical entity exploring model, to identify biomedical entities and align them to the knowledge base interactively. The model aims to automatically obtain semantic information for extracting biomedical entities and mining semantic relations through the standard biomedical knowledge base. The experiments show that the proposed method achieves better performance on entity alignment. The proposed model dramatically improves the F1 scores of the task by about 4.5% in entity identification and 2.5% in entity-concept mapping.

Automated summary

Biomedical entity alignment, consisting of entity identification and entity-concept mapping, is crucial in biomedical text mining. The proposed biomedical entity exploring model improves performance by automatically extracting semantic information and aligning entities to the knowledge base, achieving better F1 scores in both tasks.