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.

Powering the data stack

Enterprise Knowledge Graphs are the center of all major enterprise data initiatives

Data Fabric

Semantic metadata to power your models

Data Lineage

Intelligently track how your data has changed

Data Quality

Automate error identification through semantics

Data Pipelines

Manage repeatable, dynamic ETLs

Data Catalogs

Ensure discovery of data assets

Data Lakes

A semantic layer to active your data

The Knowledge Graph advantage

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? Enterprise Knowledge Graphs enable insights that are:


Central, collaborated upon understanding of data relationships and connections to power discovery and machine learning

Flexible & Extensible

Based on open source standards to dynamically pull in information from external sources or across the organization to a rich, informed data model


Semantics helps experts share, users identify, and machines use information the way the human brain thinks

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.

The TopQuadrant Difference


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


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

Reliable & Clean Data

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

Data Pipelines

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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