Google popularized the term "knowledge graph" in this 2012 blog post. Since then, there has been a massive momentum around ...

So, calling knowledge encoded on top of a graph structure a "knowledge graph" sounds natural. And the people doing this, the data modelers, have been called knowledge engineers, or ontologists.

Representation is in every AI solution, and with a good representation that captures knowledge about the business decision, the data and the analytics that deliver answers for decision making, AI ...

Key characteristics of a knowledge graph. ... The ontology is easily "extended and revised as new data arrives" due to its representation in a graphical format.

This is something that holds great promise as a way to get the best of both worlds: Curated, top-down knowledge representation (knowledge graphs), and emergent, bottom-up pattern recognition ...

Therefore, we will ask an LLM to create the knowledge graph. Image from author, June 2024 Of course, it’s the LMI framework that efficiently guides the LLM to perform this task.

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

As the knowledge-representation graph continues to grow, linking more entities and establishing their relationships to each other, more connections will be available. And since the algorithms can ...