How Data Scientists Use Knowledge Graphs to Connect Disparate Data
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
Episode 55 of The Data Science Podcast with Fexingo explores knowledge graphs — a technique that links siloed data sources into a unified, queryable structure. Lucas and Luna walk through the basics of how knowledge graphs represent entities and relationships, then dive into a concrete example: how a hospital chain used a knowledge graph to combine electronic health records, lab results, and social determinants of health to reduce hospital readmission rates by 18 percent over two quarters. They discuss construction challenges, trade-offs versus traditional relational databases, and the growing role of graph databases like Neo4j and Amazon Neptune. The hosts also touch on how companies like Google and LinkedIn have used knowledge graphs to power search and recommendation. No abstractions — just a clear, case-driven walkthrough of a technique that is becoming essential for data teams dealing with messy, interconnected data. #KnowledgeGraphs #GraphDatabases #Neo4j #AmazonNeptune #DataIntegration #HealthcareData #HospitalReadmissions #LinkedData #EntityResolution #GraphQueryLanguages #Cypher #SPARQL #DataSilos #MachineLearning #FeatureEngineering #Technology #DataSciencePodcast #FexingoBusiness Keep every episode free: buymeacoffee.com/fexingo
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