Ontology: Hype or Sustainable?

In my latest contacts with clients, they keep coming up with an ontology. The question then arises: is this a new hype or a solid and essential concept for the future? The client’s question proves the increasing interest in ontologies in recent years, due to the need for semantic clarity and data integration. It is not a new concept but has been a fundamental part of science for decades.

The concept of ontologies emerged from a philosophy concerned with the nature of existence and the categorization of things. In the 1990s, ontologies were an important part of information, artificial intelligence, and semantics. It is not a new phenomenon, but the link with AI makes clear the revival of a proven and sustainable approach.

With an increasingly complex and diverse data landscape, an Ontology provides a structured way to capture the tokens of data and define relationships between entities. Problems such as data silos, interoperability or semantic inconsistency are not temporary. If we collect or share data, these problems will persist. Using an ontology provides a sustainable solution to these challenges.

Ontologies play also a central role to make the semantic web machine readable. Data on the semantic web continues to grow and is becoming increasingly important in domains such as healthcare, government and science. The use of ontologies, in these domains, is not temporary but essential for its functioning. It ensures the linking of information in an ever-growing information landscape.

Natural language processing (NLP), knowledge graphs and improving data interpretation are increasingly being used and use ontologies. It’s not a temporary demand but an evolutionary step in the development of AI.

Moreover, in a data landscape with strict regulations (such as GDPR) and the need for data governance, ontologies have become a valuable tool. They help to catalogue, classify and manage data. A need that will not disappear in an increasingly data-driven world.

Although ontologies are currently “hot”, we cannot describe them as a hype but rather as a steadfast and lasting part of the data landscape. They offer the possibility to understand, integrate and use data in a structured and semantically rich way. Their relevance will only increase as data becomes more complex and critical. It’s a natural evolution in response to the increasing complexity of data and an essential tool for the interoperability of systems.