Semantic Modeling for Data

Chapter 1. Mind the Semantic Gap

Our agreement or disagreement is at times based on a misunderstanding.

Mokokoma Mokhonoana

In the era of the big data and AI frenzy, data is considered a gold mine that awaits organizations and businesses that will find and extract their gold. Whether you call this data science, data analytics, business intelligence, or something else, you can’t deny that data-related investments have increased significantly, and the demand for data professionals (engineers, analysts, scientists, etc.) has skyrocketed.

Do these professionals manage to find gold? Well, not always. Sometimes, the large ocean of data that an organization claims to have proves to be a small pond. Other times, the data is there but it contains no gold, or at least not the kind of gold that the organization can use. Often it is also the case that both data and gold are there, but the infrastructure or technology needed for the gold’s extraction are not yet available or mature enough. But it can also be that data professionals have all they wish (abundance of the right data, gold to be found, and state-of-the-art technology) and still fail. The reason? The semantic gap between the data supply and the data exploitation side.

Let me explain. As data practitioners, many of us work mainly on the data supply side: we collect and generate data, we represent, integrate, store, and make it accessible through data models, and we get it ready for usage and exploitation. Others of us work mainly ...