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- Deep semantic entity linkingPublication . RUAS, PEDRO; Couto, Francisco José MoreiraKnowledge organization systems, such as ontologies and knowledge graphs, are essential for organizing biomedical and clinical information and data. However, the growing volume of available scientific literature raises challenges in their maintenance. Entity linking approaches assist humans in curation by mapping entities described in text to entries of the knowledge organization systems, but their lack of coverage originates unlinkable or NIL entities. Besides, the state-of-the-art depends on deep learning models trained on large amounts of human-annotated data, which is hard to acquire. The present research work focuses on tackling these limitations of human-annotated data in the biomedical entity linking task. First, it addresses the lack of coverage of biomedical knowledge organization systems by using relation extraction to find missing relations and focusing on the problem of the NIL entities. Relation extraction increases the semantic information available for graph-based entity linking approaches (REEL), and focusing on the partial mapping of NIL entities (i.e. NIL entity linking) also improves the performance of such approaches (NILINKER). Second, the research work proposes a new deep learning model trained on a large-scale training dataset generated through automatic methods. The model is part of the pipeline X-Linker integrating different entity linking models, providing more flexibility and performance. The pipeline achieved state-of-the-art performance in the biomedical entity linking task in several datasets (BC5CDR-Disease, BioRED-Chemical, NCBI Disease). The described approaches and several others focusing on related tasks, such as named entity recognition, text classification, and recommendation of biomedical entities, were applied to several case studies, including competitions, workshops and challenges.