During the 1970s, virtually all large management information systems stored their data in some type of hierarchical or relational database.
The ideal representation for a knowledge base is an object model (often called an ontology in artificial intelligence literature) with classes, subclasses and instances.
The data in early expert systems was used to arrive at a specific answer, such as a medical diagnosis, the design of a molecule, or a response to an emergency.
A more precise statement would be that given the technologies available, researchers compromised and did without these capabilities because they realized they were beyond what could be expected, and they could develop useful solutions to non-trivial problems without them.
Representing that George, Mary, Sam, Jenna, Mike,... and hundreds of thousands of other customers are all humans with specific ages, sex, address, etc.
On the other hand, the large database vendors such as Oracle added capabilities to their products that provided support for knowledge-base requirements such as class-subclass relations and rules.
It was no longer enough to support large tables of data or relatively small objects that lived primarily in computer memory.
Knowledge management products adopted the term "knowledge-base" to describe their repositories but the meaning had a big difference.