Graphs Are Fundamental to Modern AI Systems

Artificial Intelligence (AI) is poised to drive the next wave of technological disruption across nearly every industry.



Neo4j customers are demonstrating that graph database technology brings tremendous value to AI and machine learning projects – especially in the area of knowledge graphs, which add essential context for AI applications.

Fast Track

  • How Graph Technology is Changing Artificial Intelligence and Machine Learning

    Neo4j's Amy Hodler and Jake Graham, speaking at GraphConnect 2018, talk about how using graph technologies in AI and ML applications improves accuracy, modeling speed and accessibility.

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  • Neo4j & Expero: Lower Risk & Stop Fraud Using Graph-Enhanced Machine Learning & AI

    Understand how successful financial services, banks and retailers are using graph technology and embedding intelligence to quickly identify risk and fraud patterns as they evolve.

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  • eBay’s ShopBot Delivers Recommendations with Artificial Intelligence & Neo4j

    eBay’s Chief Product Officer explains how he turned to graph technology because existing product searches and recommendation engines were unable to provide contextual information within a shopping request.

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Business Outcomes

Real-time responsiveness

Whether your AI solution is chatting with customers or driving an autonomous vehicle, real-time feedback loops are essential for meaningful machine learning that evolves your intelligence models.

Evolves with business requirements

In the emergent industry of AI, business and user requirements are still being defined, tweaked or completely upended. The graph data model is more agile and flexible than a traditional RDBMS in meeting these new and changing requirements.

Challenges

Disparate (and changing) data sources

Your machine learning algorithms consume data from a variety of ever-changing sources and data types, meaning you need a database with a versatile and adaptable schema.

Multiple hop queries

In order to determine context for the most appropriate action, AI solutions must query several layers deeper within their databases than previous technologies required. This means the database layer must be able to support multi-hop (4+) queries without affecting performance.

Why Neo4j?

Native graph store

Neo4j natively stores interconnected data – neither linear nor purely hierarchical – so it’s easier to decipher your data.

Flexible schema

Our versatile property graph model makes it easier for organization to evolve machine learning and AI models.

Performance and scalability

Neo4j supports high-performance graph queries on large datasets enabling real-time AI solutions.

Popular Graph Technology Use Cases for AI

Knowledge Graphs

Provide Rich Context for AI

AI Visibility

Human-Friendly Graph Visualization

Graph System of Record

Maintain a Source of Connected AI Truth

Graph-Enhanced AI Models—Learning

Faster, More Accurate Development

Graph-Enhanced AI Models—Execution

Operationalize Real-Time OLAP and Monitoring

Graph Analytics

Enrich AI Inputs with Graph Algorithms

Learn More About AI & ML Use Cases

Datanami: Why Knowledge Graphs Are Foundational to Artificial Intelligence
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Computer Business Review: Creating The Most Sophisticated Recommendations Using Native Graphs
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Neo4j & Expero, Inc.: Thwart Fraud Using Graph-Enhanced Machine Learning & AI
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Neo4j Machine Learning Extensions
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David Mack on Medium: Review Prediction with Neo4j and Tensorflow
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Neo4j Blog: Machine Learning, Graphs & the Fake News Epidemic
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Ready to get started?

Your enterprise is driven by connections – now it's time for your database to do the same. Click below to download and dive into Neo4j for yourself – or download the Graph Algorithms white paper to learn about these building blocks for better AI context.

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