Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb.

How do you solve the age-old data integration issue? We addressed this in one of the first articles we wrote for this column back in 2016. It was a time when key terms and trends that dominate today’s landscape, such as knowledge graphs and data fabric, were under the radar at best.

Data integration may not sound as deliciously intriguing as AI or machine learning tidbits sprinkled on vanilla apps. Still, it is the bread and butter of many, the enabler of all cool things using data, and a premium use case for concepts underpinning AI, we argued back then.

The key concepts we advocated for then have been widely recognized and adopted today in their knowledge graph and data fabric guise: federation and semantics. Back then, the concepts were not as widely adopted, and parts of the technology were less mature and recognized. Today, knowledge graphs and data fabrics are top of mind; just check the latest Gartner reports.

The reason we’re revisiting that old story is not to bask in some “told you so” self-righteousness, but to add to it. Knowledge graphs and data fabrics can, and hopefully will, eventually, address data integration issues. But what about application integration? Could graphs and ontologies help with that, too?

Read the full article on ZDNet