Blog: Why New AI Tools Like ChatGPT Need Knowledge Graphs
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Give meaning to your data

Knowledge Graphs are flexible data structures that connect “similar to” data elements, capturing relationships between data in the same way that our brains think. This structure turns your data into machine-understandable information. At the enterprise level, this enables your company to stitch together collective knowledge, break down data silos, and discover what is known across your organization.

The Knowledge Graph

The classic data structure, a relational database, has its limitations. While you can store a lot of facts and figures, at scale discovery becomes impossible. Moreover your team’s intuition is limited. How can researchers find all things to do with “cancer” when they search “tumor” or “oncology” to build the next drug?

Helping you avoid the software graveyard

Knowledge Graphs are able to accommodate diverse data and metadata that adjusts to solve real-world problems. No more science projects. Knowledge Graphs power business applications to enable cybersecurity, data privacy, drug discovery, and many others by the same principle - a shared understanding of your data.

Knowledge Graphs are self-descriptive

The meaning of the data is stored alongside the data in the graph, in the form of the ontologies or semantic models. This makes Knowledge Graphs self-descriptive.

Enterprise Knowledge Graphs enable insights that are:

The TopQuadrant Difference

Helping organizations be intentional about what their data is and how it should be used

Insights are only good as the quality of data. Enable your scientists to make your data work for you

Collaboration is more than version control. Experts, creators, administrators all can work together to shape how the organization thinks

Playing well with others is core to what we do. A wide array of partnerships and APIs out of the box help you keep your data at source and build better, faster

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