Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role. Over the past decade, there has been endless churn in technologies ...

Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, providing a way of navigating applications and services using natural language. By tying our large language models ...

To make AI possible, you need to create connections between vast quantities of data. That’s where tech like graph databases come into play. Graph databases handle fast-changing, interconnected data ...

Through natural language queries and graph-based RAG, TigerGraph CoPilot addresses the complex challenges of data analysis and the serious shortcomings of LLMs for business applications. Data has the ...

Knowledge graphs were the hottest thing in AI architecture a year ago. But today, most teams have quietly shelved them for ...

AI agents need to understand how businesses work. Graphify’s rise shows why ontologies and knowledge graphs matter for the agentic enterprise and economy.

For decades, enterprise data infrastructure focused on answering the question: “What happened in our business?” Business intelligence tools, data warehouses, and pipelines were built to surface ...

Machine learning, graph databases and multi-cloud -- William McKnight, president of McKnight Consulting Group, sees these as some of today's top enterprise BI and analytics trends. McKnight advised IT ...

In the realm of machine learning (ML), a knowledge graph is a graphical representation that captures the connections between different entities. It consists of nodes, which represent entities or ...