Graph technology’s role in AI applications

Knowledge graphs may be one way to demonstrate how ‘old school’ human intelligence works to make AI useful for the business and technology world…

Historically, businesses stored vast amounts of data in one centralized database. As data accumulates, it’s challenging for businesses to make sense of their data when they have to potentially sift through millions of records. Knowledge graphs, however, bridge disparate data points to reveal valuable insights.

For instance, in financial services, knowledge graphs are instrumental in fraud detection, where they identify hidden fraud networks, reduce false positives, and safeguard businesses from financial losses. In retail, they provide a 360-degree view of customer behavior, enabling businesses to personalize experiences, anticipate customer needs, and deepen relationships across all touchpoints.

Even in tourism, knowledge graphs have redefined content creation. Tourism Media, for example, achieved an 8x boost in productivity after transitioning to Neo4j AuraDB on AWS, allowing them to create custom travel content for over 300,000 cities in a single day—a task that previously took a full week. This automated content is not only optimized for SEO, but also enhances both efficiency and reliability – a simple yet impactful use case illustrating how knowledge graphs scale content creation to meet high-volume needs.

How do you see the relationship between AI usage and knowledge graphs evolving?

Payne: According to Gartner’s ‘Exploring the Top Use Cases for Graph Analytics’ report from May 10, 2024, over 50% of all inquiries received around AI and machine learning were about knowledge graphs – showing an increasingly intertwined relationship between AI and graph technology.

One of the most promising developments is how knowledge graphs address the challenge of AI hallucinations. Through the GraphRAG (Retrieval-Augmented Generation) approach, organizations can ground their AI models in factual information. This works by first retrieving relevant information from the knowledge graph before using it to generate AI responses, significantly improving the reliability and accuracy of AI outputs.

Moreover, as the regulatory landscape around AI usage continues to evolve, knowledge graphs will play a vital role in ensuring compliance and transparency. With more stringent regulations, organizations face increasing pressure to ensure they can trace the sources of information used by AI models. Knowledge graphs address the “black box” nature of many AI models by providing clear data lineage and traceability. The depiction of interconnected data points effectively documents the data process that powers AI models.

What are some best practices for organizations looking to adopt graph databases as part of their efforts to transform data infrastructure?

Payne: Adopting graph databases can be a powerful move for organizations seeking to transform their data infrastructure, especially for applications requiring rapid, complex queries across interconnected data. Organizations should begin by defining clear use cases where graph technology will drive real value, such as in recommendation engines, fraud detection, or network analysis. Selecting the right graph database platform is essential, with careful consideration of factors like scalability, compatibility with existing infrastructure, and support for hybrid environments.

Next, it is equally important to design a flexible data model that balances performance with simplicity to make it easy to manage and fast to query. As graph databases have their own query languages, teams should also invest in learning graph-specific query languages including the newly standardized GQL and optimizing queries to handle deep data traversals efficiently.

To adopt graph databases effectively organizations must enhance the quality of their underlying data. Data often exists in silos, so it’s essential to invest time and resources to consolidate and prepare this data for effective use in a graph database. Mapping relationships in a knowledge graph can help bridge these silos, helping organizations make the most of diverse data sources.

With these best practices in place, organizations can use graph databases to enhance data-driven decision-making, optimize operations, and ultimately gain a competitive edge.

How does Neo4j collaborate with other technology providers and partners to enhance its offerings, especially in GenAI deployments?

Payne: We have been deepening our partnerships with leading technology providers such as Deloitte, Microsoft, Amazon Web Services, Docker, Langchain and Ollama to enable enterprises to optimize their GenAI applications.

Our collaboration with data processing platforms like Databricks enables seamless data transfers from these platforms into graph structures for enhanced analytics. This helps organizations process and analyze real-time insights for decision making in GenAI applications.

Additionally, native integrations with cloud platforms such as Google Cloud streamline the development and deployment of GenAI applications. By allowing developers to swiftly generate knowledge graphs from unstructured data such as PDFs, web pages and documents, it improves data accuracy and explainability in AI outputs with added context. Both integrations allow for the use of GraphRAG to enhance the accuracy and contextual relevance of AI generated responses.

Our integration within Snowflake AI Data Cloud also incorporates over 65 graph algorithms, which are applied to knowledge graphs to detect hidden patterns and relationships. Furthermore, these algorithms can be executed instantly without data extraction, gaining faster insights with a reduced learning curve – allowing effective optimization of GenAI models