The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

翻译：在对高效和有效的大数据分析解决方案的需求推动下,在将数据分析分析纳入保健行业方面已取得重大进展,知识图表(KGs)已证明在这一领域有用,并植根于许多保健应用中,以提供更好的数据代表性和知识推论,然而,在缺乏具有代表性的KG建筑分类学的同时,这一指定领域现有的若干方法不够充分且低劣,本文件是第一个提供综合分类和鸟眼的关于保健KG建筑的观点的文件,此外,对从与各种保健环境有关的学术著作中提取的最新技术进行了彻底审查,这些技术在知识提取方法、知识基础和来源类型以及综合评价协议方面受到严格评价,最后,报告并讨论了文献中的若干研究结果和现有问题,为这一充满活力的领域的未来研究开辟了前景。