An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With growing complexity of medical ontologies big generated different resources, there is a need for integrating finding relationships between distinct concepts where these have logical relationships. Standardized Medical Ontologies are explicit specifications shared conceptualization, which provide predefined vocabulary that serves as stable conceptual interface sources. Intelligent Healthcare systems such disease prediction require reliable knowledge base based on ontologies. Knowledge graphs emerged powerful dynamic representation base. In this paper, framework proposed automatic graph generation two standardized ontologies- Human Disease Ontology (DO), Symptom (SYMP) using online website encyclopedia. The methodologies adopted automatically generating fully integrated dynamic, scalable, easily reproducible, reliable, practically efficient. A subgraph cancer terms also extracted studied modeling representing diseases, their symptoms, prevention, risk factors.