Developing the Enterprise Knowledge Graph Capabilities to Win
Graph Data Science and AI Integration
Workshop
Use graph analytics to improve AI outcomes—and close the loop back to knowledge
Graph data science turns relationships into measurable signals that can enhance prediction, discovery, and GenAI grounding. This workshop.
Leave with a graph-to-AI blueprint—analytics use cases, feature strategy, integration.
The Challenge
Many organizations have knowledge graphs, but struggle to translate graph structure into usable analytics and AI signals.
- Graph insights aren’t captured: Teams query relationships, but don’t apply algorithms that reveal communities.
- ML features are hard to engineer: Graph signals and embeddings aren’t standardized, slowing model development.
- Outcomes don’t feed back into the graph: Predictions and insights stay in dashboards or models, rather than.
Without a graph-to-AI loop, the knowledge graph remains underutilized—limiting AI.
Our Solution
We guide your team through a practical approach to apply graph data science.
- Neo4j Graph Data Science Foundations: Align on core GDS capabilities and how graph algorithms support.
- Graph Algorithms for Insight and Prediction: Identify the algorithms that fit common enterprise needs (similarity, communities.
- Embeddings and Feature Generation Strategy: Define how to create, manage, and reuse graph-based features.
- Integrating ML Outcomes Back into the Graph: Establish patterns to write predictions, classifications, and signals back.
- EKGs in AI and GenAI Workflows: Define how enterprise knowledge graphs strengthen AI workflows and improve.
Area of Focus
- Introduction to Neo4j’s Graph Data Science Library
- Applying Graph Algorithms for Insights and Predictive Modeling
- Generating Graph Embeddings and Features for Machine Learning
- Integrating ML Outcomes Back into the Knowledge Graph
- Leveraging Enterprise Knowledge Graphs in AI Workflows and GenAI Applications
Participants Will
- Identify high-value graph analytics use cases that support AI, discovery.
- Select graph algorithms that reveal meaningful patterns and predictive signals.
- Define a strategy for embeddings and reusable graph features.
- Establish an approach to integrate ML outcomes back into the.
- Leave with priorities and next steps to operationalize graph-to-AI integration.
Who Should Attend:
Solution Essentials
Format
Facilitated working session
Duration
8 hours
Skill Level
Advanced
Tools
Slides, templates, worksheets, and collaboration tools.