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 ...

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 ...

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 ...

Data Squared (Data²), a small business dedicated to helping public and private sector organizations unlock the full potential of their data, is partnering with Neo4j, a leading graph database and ...

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 ...

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.

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

We had a chance to speak with TigerGraph's incoming head of product R&D, and it spurred some thoughts on where we thought graph databases should go.

NEW PRODUCT ANALYSIS: Unlike other graph databases that delve two to three levels deep into the connected data, TigerGraph's pattern analytics is tuned to be efficient and tractable with the ...