Real World Knowledge Graphs
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This document discusses building knowledge graphs and provides examples of how they are used in the financial services industry. It defines key differences between a knowledge graph and knowledge base, explaining that knowledge graphs are dynamically updated using data relationships to derive new knowledge across heterogeneous data types. The document outlines principles for building knowledge graphs and provides examples of financial services knowledge graphs for credit risk management, investment risk management, and portfolio news recommendations. It also provides information about Neo4j, the company that created the Neo4j Graph Platform.
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A Dynamic Data Catalog for Autonomy and Self-Service
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Smart Data Webinar: Knowledge as a Service
Building a successful ModernAI application often requires large volumes of data for training ML models or data that has been organized into knowledge using taxonomies or ontologies to support specific vertical markets (healthcare, insurance, pharma, etc.) or horizontal functions (HR, legal, supply chain, etc). While tools do exist to help developers ingest and organize the required data into meaningful knowledge stores, using pre-built data or knowledge packages can make application development faster, more reliable, and less expensive than starting from scratch.
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Agile Data
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2) In a case study, a traditional methodology required multiple rounds of rework when user needs changed, while an agile approach allowed rapidly loading data, running queries, and refining the solution with user feedback to group data differently.
3) Key aspects of the agile data analytics methodology include focusing on the user's value, collaborative requirements, simple and adaptable architecture, integrated cross-functional teams, and an attitude of accepting change.
Importance of Big data for your Business
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
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Real World Knowledge Graphs
- 1. A Real-World Guide to Building Your Knowledge Graphs Nav Mathur Sr. Director – Global Solutions, Neo4j, Inc. in/navmathur @nav_mathur
- 2. 2 What do these organizations have in Common? 2
- 3. 3 More real-world knowledge graphs
- 4. 4 Knowledge Graph Vs Knowledge 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 Knowledge Graph Credit Risk Management
- 7. 7 Financial Services Knowledge Graph Investment Risk Management
- 8. 8 Financial Services Knowledge Graph Portfolio News Recommendations
- 9. 9 Portfolio News Recommendations Data Model 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 Data Sources 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)
- 12. 12 720+ 7/10 12/25 8/10 53K+ 100+ 300+ 450+ Adoption Top Retail Firms Top Financial 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
- 13. Thank You