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Real World Guide to Building Your Knowledge Graph
This document discusses building and using knowledge graphs. It provides examples of knowledge graphs used in financial services for tasks like credit risk management, investment risk management, and portfolio news recommendations. It also discusses using knowledge graphs for recommendations in various domains like retail, customer journeys, personalization, and more. Finally, it discusses technologies and approaches for building intelligent recommendation frameworks using knowledge graphs.
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Real World Guide to Building Your Knowledge Graph
- 1. A Real-World Guideto Building Your Knowledge Graphs Nav Mathur Sr. Director – Global Solutions, Neo4j, Inc. in/navmathur @nav_mathur
- 2. 2 What do theseorganizations have in Common? 2
- 3.
- 4. 4 Knowledge Graph VsKnowledge Base “Unlike a simple knowledge base with flat structures and static content, a knowledge graph acquires and integrates adjacent information using data relationships to derive new knowledge.”
- 5. Connected around relevant attributes. (contextually related) Dynamically updating/ not manual Uses intelligent labelling and ties in to the graph automatically Explainable - Intelligent metadata helps traverse to find answers to specific problems, even when we don’t know exactly how to ask for it. Usually contains heterogeneous data types. It combines and uncovers connections across silos of information. Key Principles of a Knowledge Graph
- 6. 6 Financial Services KnowledgeGraph Credit Risk Management
- 7. 7 Financial Services KnowledgeGraph Investment Risk Management
- 8. 8 Financial Services KnowledgeGraph Portfolio News Recommendations
- 9. 9 Portfolio News Recommendations DataModel User Org Article Topic Topic Group HAS_TOPIC GROUP WEIGHT TRACKS REFERENCES IS_AFFILIATED SUPPLIES HAS_ULTIMATE_OWNER HAS_IMMIDIATE_OWNER
- 10. 10 Data Orchestration Layer DataSources CLIENT Admin Dashboard Session Data Feedback Scored Recommen- dations Graph Algorithms AI / ML Click Stream Data INTELLIGENT RECOMMENDATIONS FRAMEWORK Discovery Exclude Boost Diversity User Segmentation Item Similarity Recommendation Engines Building your KG • Modelling • Data Ingestion • Auto Labelling (NLP) • Scoring • Data Lineage • Alerting • Auto and human aided merging/similarity • Integrate ML for refreshing /updating the graph RSS Feed Org. Feed (Graph)
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- 12. 12 Recommendations Are Everywhere JobSearch and Recruiting Financial Services Retailing Government Services Healthcare Travel Media and Entertainment
- 13. 13 Recommendations Are Everywherein the Enterprise Human Resources Supplier Analytics Product and BOM Analytics Personalization Customer Journey
- 14. 14 Neo4j HCM UseCases Ratings Normalization Succession Planning Building Cross- Functional TeamsFlight Risk Lifetime Employee Value Promotion and Compensation Recommendations
- 15. Retail Recommendations Filter Criteria Price,brand, color… Similar Products Automated bundling and pricing Related Products
- 16. Customer 360 /Customer Journey Recommendations Profitability and Margin Analysis Dynamic CSAT Score Customer Acquisition and Retention Cost Lifetime Customer Value Engagement Analysis and Alerts Churn Score and Analysis
- 17. Personal 360 Recommendations Building Communities/ BOFs Engagement Score People Connections CCPA/GDPR Content Recommendations Discover cohorts
- 18. Customers Product and BOMRecommendations Bottleneck analysis Optimized plant assignment MBOM analysis Part/material similarity Inventory analysis Alternate vendors and suppliers Influential part / material discovery
- 19. 19 Hybrid Scoring-Based Approachis More Contextual Graph technology enables you to make recommendations that weight multiple methods Collaborative Filtering Based on similar users or products Content Filtering Based on user history and profile Rules-Based Filtering Based on predefined rules and criteria Business Strategy Based on promotions, margins, inventory
- 20. 20 Neo4j Gives YouControl of Your Online Business Self-learning framework improves recommendations over time Customer Context Recommendations Monitor and Adjust Machine Learning Feedback
- 21. Graph Algorithms Pathfinding andSearch Parallel Breadth First Search and DFS Shortest Path Single-Source Shortest Path All Pairs Shortest Path Minimum Spanning Tree A* Shortest Path Yen’s K Shortest Path K-Spanning Tree MST Centrality and Importance Degree Centrality Closeness Centrality Betweenness Centrality PageRank Harmonic Closeness Centrality Dangalchev Closeness Centrality Wasserman and Faust Closeness Centrality Approximate Betweenness Centrality Personalized PageRank Community Detection Triangle Count Clustering Coefficients Connected Components Union Find Strongly Connected Components Label Propagation Louvain Modularity 1 Step Balanced Triad Identification Louvain Multi-Step Similarity and ML Workflow Euclidean Distance Cosine Similarity Jaccard Similarity Random Walk One Hot Encoding
- 22. 22 Data Sources CLIENT AdminDashboard Session Data Feedback Scored Recommen- dations Graph Algorithms AI / ML Click Stream Data INTELLIGENT RECOMMENDATIONS FRAMEWORK Discovery Exclude Boost Diversity User Segmentation Item Similarity Intelligent Recommendations Framework Recommendation Engines
- 23. Recommendation Framework Technology EnginesAre Processing Pipelines • Pipelines mimic process of building recommendations to reduce query complexity • Easier to develop and maintain • Can enable and disable different parts of the pipeline based on business rules Promotions, inventory, etc. • Ability to score and weight phases differently • Supports traceability and explainability Recommendation Framework Advantages • Faster time to realization • Development and implementation flexibility • Code-free development Highly Configurable Engines • Works with any data model • Highly contextual recommendations for what the user is doing now • Different engines for different uses Discovery Exclude Boost Diversity
- 24. Thank You • NavMathur • Sr. Director – Global Solutions, Neo4j, Inc. • in/navmathur • @nav_mathur
- 25. 25 720+ 7/10 12/25 8/10 53K+ 100+ 300+ 450+ Adoption Top Retail Firms TopFinancial Firms Top Software Vendors Customers Partners • Creator of the Neo4j Graph Platform • ~250 employees • HQ in Silicon Valley, other offices include London, Munich, Paris and Malmö Sweden • $80M new funding led by Morgan Stanley & One Peak. Total $160M from Fidelity, Sunstone, Conor, Creandum, and Greenbridge Capital • Over 15M+ downloads & container pulls • 300+ enterprise subscription customers with over half with >$1B in revenue Ecosystem Startups in program Enterprise customers Partners Meet up members Events per year Industry’s Largest Dedicated Investment in Graphs Neo4j - The Graph Company