When we set out to build a knowledge graph at Zalando, most people did not know how to build one, or considered machine learning as the better solution. However, endorsement from upper management led to the current project, where we use ontologies to improve the customer search and browsing experience.
There are many unique things about the way we built our ontology for Enterprise purposes. Our ontology is peer-reviewed, use case-driven, and we apply special techniques to keep the graph and our APIs and data in sync.
Communicating the graph to different professionals also has its challenges. Backend engineers and machine learning experts have a hard time understanding knowledge graph quirks. Product people accept it only if it creates a clear improvement for customers. How do you reconcile them all?