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Graph AI: Improved Data Modeling by Including Contextual Relationships

By Dick Weisinger

After a decade of rapid advances in artificial intelligent, data scientists are continuing to look to how they can improve. One area that researchers are investigating is the use of graph data bases with AI.

Graph databases are particularly useful to show relationships and associations between things. Graph data can optimize the way that certain kinds of data is represented, and it can provide better accuracy and even help AI models provide a deeper degree of ‘explainability’.

Google data scientists wrote in a paper that “graphs, generally, are a representation which supports arbitrary (pairwise) relational structure, and computations over graphs afford a strong relational inductive bias beyond that which convolutional and recurrent layers can provide.”

James Kobielus, tech industry analyst, wrote for InformationWeek that “building on but not replacing these first two waves, AI’s future focuses on graph modeling. Graphs encode intelligence in the form of models that describe the linked contexts within which intelligent decisions are executed. They can illuminate the shifting relationships among users, nodes, applications, edge devices and other entities.”

From neo4j blog: How Graphs Enhance Artificial Intelligence

Frank Eaves, software engineer at Devada, wrote that “because graph databases hold data and the relationships between that data allow for easy and intuitive querying, they will continue to help industries gain deeper insights into their ever-accumulating cache of data to better serve their customers.”

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