Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

4. Document Discovery with Graph Data Science

Gary Mann, Sr. Solutions Engineer, Neo4j

  • Soyez le premier à commenter

4. Document Discovery with Graph Data Science

  1. 1. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 Document Discovery with Graph Data Science Gary Mann gary.mann@neo4j.com
  2. 2. Neo4j, Inc. All rights reserved 2021 ● Customers are already very good at search. ○ Too much data, too many tools. ○ Data is highly variable - schemaless. ○ Need to focus analysts. ○ Start with unstructured search - then traverse and discover paths through data. ● Customers want to: ○ Leverage both structured and unstructured data. ○ Support/discover multiple relationships between pieces of information. ○ Understand how entities interact ○ Navigate/Traverse across both structured and unstructured data visually. Graph-Aided Discovery
  3. 3. Neo4j, Inc. All rights reserved 2021 Graph-Aided Discovery ● Why Graph? ○ Flexible and (mostly) schemaless model ○ Unstructured queries to enter the graph ○ Queries and pattern matching to traverse relationships. ○ Pathfinding algorithms to understand how entities are related. ○ Graph Analytics to leverage the overall graph structure. ○ Graphs can form the basis for many analytical use-cases including Discovery, Analytics, Investigations, etc.
  4. 4. Neo4j, Inc. All rights reserved 2021 Query (e.g. Cypher/Python) Real-time, local decisioning and pattern matching Graph Algorithms Global analysis and iterations You know what you’re looking for and making a decision You’re learning the overall structure of a network, updating data, and predicting Local Patterns Global Computation When Do I need Graph Algorithms?
  5. 5. Neo4j, Inc. All rights reserved 2021 Questions to Answer ● Local Patterns ○ How many X are related to Y? ○ How are X and Y related through multiple hops? ○ What characteristics to Y and Z share in common? ■ Are they the same entity? MATCH p=(a:Person)-[:HAS_PHONE]->(b:Person) RETURN p ● Global Analytics ○ What are my important entities in my graph? ○ What data is related through its relationships in the graph? ○ Can I predict relationships that don’t explicitly exist?
  6. 6. Neo4j, Inc. All rights reserved 2021 The Neo4j Graph Data Science Library • Deep path analytics • Optimal routing • Evaluates how alike nodes are • Construct graphs from data Pathfinding & Search Similarity Community Detection Mutable In-Memory Workspace Computational Graph Native Graph Store 50+ Robust Algorithms Flexible Analytics Workspace • Identifies node importance • Influencer & Risk Identification Centrality / Importance • Detects group clustering • Partition options • Estimates likelihood of • Estimate missing information Link Prediction Graph Embeddings • Learn your graph topology • Use for dimensionality reduction
  7. 7. Neo4j, Inc. All rights reserved 2021 7 Supervised ML Graph-Native Feature Engineering Train Predictive Model Queries Algorithms Embeddings 1. Model Type 2. Property Selection 3. Train & Test 4. Model Selection Apply Model to Existing / New Data Store Model in Database Use Predictions for Decisions Use Predictions to Enhance the Graph Publish & Share
  8. 8. Neo4j, Inc. All rights reserved 2021 1. Ingest Data and Derive Relationships from Unstructured pieces 2. Leverage Graph Analytics to Enhance the Data/Discovery Process 3. Enable Unstructured Search on the Graph 4. Enable Visual Exploration through Neo4j Bloom 5. Tie it all together in a simple Web Application Example Use-Case
  9. 9. Neo4j, Inc. All rights reserved 2021 9 Custom Application
  10. 10. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 Demo
  11. 11. Neo4j, Inc. All rights reserved 2021 ● Graphs can provide context to information discovery applications. ○ Data relationships are key ○ Both structured and unstructured data ○ Exploration and analytics ○ Focus analysts along paths ○ Graph Data Science algorithms used to enhance the graph. ● Enhanced Document Graphs can form the basis of many discovery and analytical uses cases. Takeaways
  12. 12. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 12 Thank you! Contact us at gary.mann@neo4j.com

×