Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.

A knowledge graph, is a graph that depicts the relationship between real-world entities, such as objects, events, situations, and concepts. This information is typically stored in a graph database ...

Using medical knowledge graphs in smart applications for clinical diagnoses and more Based on a Google algorithm technique, this way of organizing data can have wide-reaching implications for the ...

Sometimes, you can enter into a technology too early. The groundwork for semantics was laid down in the late 1990s and early 2000s, with Tim Berners-Lee's stellar Semantic Web article, debuting in ...

Using knowledge graphs with large language models But a knowledge graph is only half the story. LLMs are the other half, and we need to understand how to make these work together.

Graph Algos Frame has first-hand experience with this phenomenon. Prior to joining Neo4j, Frame worked as a data scientist for the federal government, where she worked extensively with the Neo4j ...

Graph data science is an emerging field with a lot of promise, but it’s being hamstrung by the need for practitioners to have lots of data engineering and ETL skills. Now Neo4j is hoping to drive that ...

Neo4j said the collaboration will enable Azure users to structure unstructured data and load it into a knowledge graph. From there, they can use Neo4j tools such as Bloom data visualization or the ...