  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.
