Learn how to build an F1 teammate graph in Neo4j, derive relationships from race data, and connect Max Verstappen to Juan Manuel Fangio with Cypher.

Remote: United States

Listen to (or read) Felienne Hermans’ engaging talk from GraphConnect Europe on why you need to think about spreadsheets as source code – and not data.

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著者：クリエーションライン株式会社(監)/李昌桓(著)　出版：インプレス NextPublishing　2015/10/30(金)配信

Knowledge graphs can give enterprise agents a shared understanding of how data, business rules and processes fit together. However, that context becomes harder to maintain when each new agent carries its own version of what the business knows. Organizations have become more proficient at building agents, but their results still vary widely. The difference often lies in how enterprise knowledge is presented, according to Jesús Barrasa (pictured), field chief technology officer of Gen AI at Neo4j Inc. “You have to give agents not only access to your data, but also to your meaning, to your enterprise knowledge,” he said. “That’s unfortunately not always well captured … [or] well represented.” Barrasa spoke with theCUBE Research’s John Furrier at the GraphSummit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how a shared knowledge layer could help agents use enterprise context, explain their answers and reuse knowledge across tasks. (* Disclosure below.) Why knowledge graphs need a shared layer As organizations expand their use of agents, they face a familiar problem: Work done for one application does not necessarily carry over to the next. Barrasa sees a parallel with earlier reporting systems that produced conflicting results. “They identify a...

Neo4j frames knowledge graphs as shared context for AI agents, helping enterprises reuse business knowledge, trace answers and measure returns.

This article is a generalization of the approach designed by Eric MONK in his article “Mix and batch: a technique for fast, parallel…

Testing Jev (TypeSafe System One) with Navigation Decisons I built neo4jev as an experiment to navigate a graph, here I explain a bit the why and how. Streamlit app in neo4jev that allows database…

Query, model, visualise and analyse graph databases with ease. The ultimate data studio for Amazon Neptune, Spanner Graph, Neo4j, GraphDB and 30+ others.

This post records the real reasoning chain automatically (zero changes to your tools), stores it in Neo4j with the graph vendor's own agent-memory SDK, and runs the reverse audit: when a source turns out wrong, one graph traversal returns every decision that touched it, at read time, where a flat log would scan every record

Somewhere in your company's ServiceNow instance is the answer to the question an engineer asks at two in the morning: if this is broken, what else is about to break? Every fact needed to answer it has