Using Knowledge Graph to Enhance Understanding of Medical Terminologies

Abstract

Graphs are currently the foundation of many applications, ranging from search engines and recommendation systems to intelligent chatbots. The Graph data model represents relationships between entities by connecting them via edges based on information derived from many different sources. Once the data is in graph format, several graph analytic techniques may be used to query multi-hop relationships between entities in the generated KG. Knowledge graphs have shown to be an efficient way for mapping linkages between the large diversity and structure of healthcare data in the healthcare services sector. Graphs have an incredible capacity to represent hidden links between information sources and capture linked information (i.e., entity relationships) that other data models are incapable of capturing