The data is then converted into a format suitable for AI algorithms, such as numerical values, vectors or graphs. Data structuring techniques vary based on the type and purpose of the data.

Available starting today, Neo4j Aura Graph Analytics is said to work with any kind of data source, including Oracle, Microsoft SQL, Databricks, Google BigQuery, Snowflake and Microsoft OneLake.

Extracting valuable information from big data requires innovative approaches that efficiently process large amounts of data as well as handle and, moreover, utilize their structure. This article ...

CMO Guide To Schema: How Your Organization Can Implement A Structured Data Strategy Future-proof your brand’s search strategy with schema and structured data markup, and ensure your web data is ...

After we have defined the fundamental elements of our graph, let’s implement the actual Graph data structure. // Graph is a map of all the nodes in the graph represented by // their string ...

Explore the role of content knowledge graphs in enhancing your marketing strategy and improving information retrieval with structured data.

GraphRAG’s performance heavily depends on the quality of its graph database. This foundation involves selecting or constructing appropriate graph data, ranging from open knowledge graphs to ...

Home Brandposts Unlock growth and efficiencies with EXL's Generative AI Solutions Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG Unlock growth and efficiencies ...

A Graph Neural Network (GNN) conducts the graph convolution for structured data and obtains the weighted sum over the vertices according to its graph structure. However, in the context of a wireless ...