Knowledge-graph-labs

Collection of learnings and materials on graph databases and knowledge graphs

Workshop with Neo4j+Deloitte+Google

Neo4j

• Community of data scientists
• Established industry experience accross verticals
• Networks of people
• Transaction networks
• Knowledge networks
• Connections starts to take on shapes
  • Small, wide data
  • Complex data
  • Hierarchical
• Questions
  • Whats important (What customer want to buy)
  • WHats unussual (Fraud detection, anomaly detection)
  • Whats next (Predicting the best route, predicting next machine that require maintenance)
• Why use native graph database
  • Performance at scale
  • RDB cannot model or stored data witout complexity
  • Performance degrades with number and levels of relationships and databse size
  • Query complexity grows with need for JOINs
• Anatomy of a property graph
  • Stores data as graph
  • Nodes, like Person and Car
  • Relationships, like Drives, Owns, Married To
  • Properties, like volvo, model, latitude, long or name, born, twitter

References

• https://www.kaggle.com/learn-guide/5-day-genai
• https://www.deloitte.com/nl/en/services/risk-advisory/perspectives/responsible-enterprise-decisions-knowledge-enriched-ai.html
• https://github.com/neo4j-partners/neo4j-sec-edgar
• https://storage.googleapis.com/neo4j-datasets/hands-on-lab/form13-2023.csv
• https://storage.googleapis.com/neo4j-datasets/hands-on-lab/form13-2023-05-11.csv