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.

生物医学实体比对由两个子任务组成：实体识别和实体概念映射，在生物医学文本挖掘中具有重要的研究价值，而这些技术被广泛用于名称实体标准化，信息检索，知识获取和本体构建。

先前的工作在特征工程方面做出了许多努力，以采用基于特征的模型进行实体识别和对齐。但是，依赖于主观特征选择的模型可能会出现错误传播，并且无法利用隐藏信息。随着健康相关研究的迅速发展，研究人员需要一种有效的方法来探索大量可用的生物医学文献。

因此，我们提出了一个两阶段的实体对齐过程，即生物医学实体探索模型，以识别生物医学实体并将其与知识库进行交互式对齐。该模型旨在通过标准生物医学知识库自动获取语义信息，以提取生物医学实体并挖掘语义关系。实验表明，该方法在实体对齐方面取得了较好的性能。所提出的模型在实体识别中将任务的F1分数显着提高了约4.5％，在实体概念映射中则提高了2.5％。