WEIGHTED ENTITY-LINKING AND INTEGRATION ALGORITHM FOR MEDICAL KNOWLEDGE GRAPH GENERATION

Abstract

: Semantic data integration is the process of interrelating information from multiple heterogeneous resources. There is a need for representing data concepts and their relationships to eliminate heterogeneity among different data sources in healthcare management systems. Standardized medical ontologies provide predefined medical vocabulary serving as a stable interface for concepts related to medical data sources. However, different ontologies have different concepts although these concepts have logical relations between them such as the Human Disease Ontology and the Symptoms ontology. There aroused a need for a knowledge graph providing a reliable knowledge base for any intelligent healthcare expert advisor disease prediction system. The knowledge graph provides a model for linking and integrating different concepts having logical relationships such as diseases and their symptoms. Medical online website and encyclopedia provides a reliable source for building such a knowledge graph. The knowledge graph is enriched with social networks data where information extracted reflects a major source of data based on user experiences. The paper proposes a framework for constructing a disease-symptom entity linked knowledge graph based on online medical encyclopedia and social networks user experiences. Entity linking such an integrated knowledge graph with standardized medical ontologies makes it a reliable knowledge base for a standard system that could be used by social networks user and the professional staff.