Knowledge graphs—machine-readable data representations that mimic human knowledge—are bridging the gap between proprietary enterprise data and safe, reliable, helpful LLMs.

Graph analytics and knowledge graphs facilitate scientific research for COVID-19 State of the art in analytics and AI can help address some of the most pressing issues in scientific research.

In other words, a knowledge graph is a programmatic way to model a knowledge domain with the help of subject-matter experts, data interlinking, and machine learning algorithms.

Knowledge graphs help in organizing unstructured data in a way that information can easily be extracted where explicit relations between multiple entities help in the process.

Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query ...

When enhanced by the rich, self-describing nature of semantic knowledge graphs, data mesh and data fabric can greatly complement one another.

Symbolic reasoning Knowledge graphs are at the basis of symbolic reasoning systems using expert rules for real-life business problems. Regardless of the particular domain, data source, data format ...

Knowledge (i.e., meaning): Concepts and the relationships between the concepts are first class citizens, meaning that it encodes knowledge of how the domain users understand the world. Graph (i.e., ...

The Bulgarian graph database startup Graphwise today announced a major upgrade to its flagship GraphDB tool, adding new features aimed at boosting enterprise knowledge management and creating a more ...