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How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications: Research by Gartner®

Knowledge graph projects are increasing due to rising awareness, clearer benefits, and more accessible building tools. Yet, many teams struggle with getting started and getting a knowledge graph into production.

The Gartner report, How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications, published in September 2022, states that “knowledge graphs deliver semantically enabled data management to power a diverse range of AI applications.” No wonder there’s such interest, and this report provides practical guidance on starting.

One of the key takeaways is the need for data and analytics leaders to focus on the opportunities to use the technology to deliver AI applications beyond just connecting data silos. Many knowledge graph experts have been touting the need to bring together data for so long that they forget to help businesses see how this tactically adds value.

Understandably, the focus is on the data because the flexible nature of knowledge graphs helps ensure that information is consistently understood and used across an organization. That makes it easier for business users, software engineers, and data scientists to access the data they need - and be assured of its correctness regardless of its use.

However, when it comes to illustrating value today, big business wins are in AI and intelligent applications. Here, we can use knowledge graphs on their own to feed AI systems contextual data, or we can use them together with machine learning to bring together composite AI capabilities.

This Gartner report provides foundational guidance, such as using a Minimal Viable Ontology and Graph (MVO and MVG shown in the lead image), which is constructive for those tricky initial steps. Also covered in the report are critical use cases relying on knowledge discovery, semantic search, recommendations, and decision intelligence platforms.

Finally, the conceptual architecture in the report, illustrated below, shapes considerations on how knowledge graphs and machine learning can be used together, helping us imagine even more potential use cases.

Our team feels it’s important to look at integrating knowledge graphs with cloud data platforms using a relational knowledge graph, so that any data professional or domain expert can easily express, use, and share knowledge through intelligent data applications.