These graphs can also help the model to reduce bias and hallucination, provide explainability and to secure your sensitive knowledge. They can then perform tasks using data they weren't trained on. GraphBase knowledge graphs can be used to provide external context and content for these models. Machine learning is getting plenty of press, particularly Generative AI and the Large Language Models (LLMs) that enable it. What is Artificial Intelligence (AI)? We create AI when we give a machine the capacity to turn data, observations and communication into knowledge - and then use that knowledge to perform useful tasks. It's a powerful and liberating way to work with data and it gives you power to create applications and solve problens in ways that aren't possible with any other data store. Large or small whole graphs can be cut, combined and manipulated in many ways. You get a graph equivalent of the "rows and tables" paradigm that makes a Relational Database so easy to use. In GraphBase, the graph is a first-class citizen. We achieved this by redefining how graph data should be managed. With GraphBase, our goal was to simplify the management of complex data structures, so that your data could become something more. ![]() They're powerful tools, they have many uses, but they're still not suited to the management of complex data structures. The current crop of graph database products - the triplestores and property graphs - have been around for nearly two decades. A graph database provides much better modelling utility, performance and scalability. GraphBase is a Graph Database Management System (Graph DBMS) engineered to simplify the creation and maintenance of complex data graphs.Ĭomplex and highly-connected structures are a challenge for the Relational Database Management System (RDBMS).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |