Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions in articles to entities in a certain knowledge base (e.g., the Unified Medical Language System), draws dramatic attention in recent years. BM-EL can help to disambiguate medical terms and link to rich semantic information in the biomedical knowledge base, which can act as an essential means for many downstream applications.Although entity linking tasks have been investigated in the general domain and achieved great success, many challenges remain in the biomedical field, for instance, highly complex terminology, less training data, and entity ambiguity.In this survey, we categorize BM-EL methods into rule-based, machine learning, and deep learning models according to the development of the model paradigm and provide a comprehensive review of each approach.In-depth study of current BM-EL efforts, we group the model architectures into four categories: joint entity recognition and linking, graph-based global entity disambiguation, cross-lingual architectures, and model-efficiency improvement.We further introduce six well-established datasets that are commonly used for BM-EL tasks. Furthermore, we present a comparison of the different methods and discuss their advantages and disadvantages.Finally, we discuss the limitations of existing methods for BM-EL and discuss promising future research directions.

Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions in articles to entities in a certain knowledge base (e.g., the Unified Medical Language System), draws dramatic attention in recent years. BM-EL can help to disambiguate medical terms and link to rich semantic information in the biomedical knowledge base, which can act as an essential means for many downstream applications.Although entity linking tasks have been investigated in the general domain and achieved great success, many challenges remain in the biomedical field, for instance, highly complex terminology, less training data, and entity ambiguity.In this survey, we categorize BM-EL methods into rule-based, machine learning, and deep learning models according to the development of the model paradigm and provide a comprehensive review of each approach.In-depth study of current BM-EL efforts, we group the model architectures into four categories: joint entity recognition and linking, graph-based global entity disambiguation, cross-lingual architectures, and model-efficiency improvement.We further introduce six well-established datasets that are commonly used for BM-EL tasks. Furthermore, we present a comparison of the different methods and discuss their advantages and disadvantages.Finally, we discuss the limitations of existing methods for BM-EL and discuss promising future research directions.

Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions in articles to entities in a certain knowledge base (e.g., the Unified Medical Language System), draws dramatic attention in recent years. BM-EL can help to disambiguate medical terms and link to rich semantic information in the biomedical knowledge base, which can act as an essential means for many downstream applications. Although entity linking tasks have been investigated in the general domain and achieved great success, many challenges remain in the biomedical field, for instance, highly complex terminology, less training data, and entity ambiguity. In this survey, we categorize BM-EL methods into rule-based, machine learning, and deep learning models according to the development of the model paradigm and provide a comprehensive review of each approach. In-depth study of current BM-EL efforts, we group the model architectures into four categories: joint entity recognition and linking, graph-based global entity disambiguation, cross-lingual architectures, and model-efficiency improvement. We further introduce six wellestablished datasets that are commonly used for BM-EL tasks. Furthermore, we present a comparison of the different methods and discuss their Knowledge-Graph-Enabled Biomedical Entity Linking: A Survey advantages and disadvantages. Finally, we discuss the limitations of existing methods for BM-EL and discuss promising future research directions.

A Cross-Validated combined forecasting method is proposed to retain more data information of wind power at each time level, so that the wind power forecasting results at different time scales meet the aggregation constraints. We used the actual data of a wind farm in Shandong Province to verify the effectiveness of the method. The results show that compared with the existing method, forecasting with Cross-Validated combined increases accuracy over conventional forecasting, particularly in different time levels with larger sampling interval.