As 2022 dawns, knowledge graphs bear the dubious distinction of being at the epicenter of AI and machine learning for two reasons. One is that, unassisted, they are one of the myriad manifestations of AI due in part to their highly contextualized understanding of the relationships between data, enterprise knowledge, and the terms that populate both. Second, they’re perhaps the most creditable means of assembling all data at the enterprise’s disposal—regardless of structure variation, type, or format— for building the machine learning models whose predictive and prescriptive power makes AI so desired by end users. In this regard, they’re either the foundation of the popular data fabric tenet or a crowning layer for integrating the data connected by this framework…
Knowledge graphs provide three key functions for AI and machine learning, according to Kendall Clark, CEO of Stardog. “There’s an analytics capability on one side, and a data integration or data prep capability on the other side. And you can think about the interaction of them as almost a third capability,” he said.
Read the complete KMWorld article, “Turning data into gold: Knowledge graphs, AI, and machine learning.”
For more on ML from the Stardog blog see, “Knowledge Graphs and Machine Learning.”
