By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

By multi-model, Franz said its semantic graph database supports ingestion of different JSON documents as well as Resource Description Framework (RDF), or triplestore, data—another World Wide Web ...

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...

Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

Unlike relational databases, which work particularly well with structured data, graph databases are designed to model and store data as interconnected nodes and relationships.

Companies traveling the long road to becoming data-driven organizations should take a close look at why Graph Databases are taking master data management to a new level.

A new semantic-based graph data model has emerged within the enterprise. This data model has all of the advantages of the relational data model, but goes even further in providing for more ...

New techniques make graph databases a powerful tool for grounding large language models in private data.

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.