Ontology: From 17th-Century Neologism to Tech Darling

How My Training in Heidegger Prepared Me for Crafting Knowledge Architectures

I didn’t receive a recruiter’s email inviting me to become an “ontologist.” Yet the name fits: I spend my days helping people design information architectures, clarifying what entities populate their systems and defining the relationships that bind them. In teaching people how to model their domain, I realized I was doing applied ontology. My doctoral work on Heidegger’s fundamental ontology taught me not only to ask what there is but also how those things interrelate—what world they constitute—a skill now crucial in technology and business.

Aristotle’s Metaphysics investigates “being qua being” (τὸ ὂν ᾗ ὂν), exploring the most general features of reality. Yet he never labels this inquiry ontology. The term would not appear until 1606, when German scholar Jacob Lorhard coined ontologia in Ogdoas Scholastica, defining it as scientia entis in genere—the science of being in general. A decade later, Rudolf Goclenius included the word in his Lexicon philosophicum (1613), planting it in early-modern academic discourse. This neologism united Greek onto- (being) with -logia (account), providing a succinct handle for what scholars had discussed under the cumbersome label metaphysica generalis. Although obscure at first, ontology would soon anchor the curriculum of European universities.

The 18th-century rationalist Christian Wolff elevated ontology with his Philosophia Prima sive Ontologia (1730), teaching it as the deductive foundation of all other sciences. Under Wolff’s influence, every student of metaphysics learned to treat ontology as the science of essences and first principles. But the triumph provoked backlash. David Hume challenged the coherence of a priori ontology, and Immanuel Kant’s Critique of Pure Reason (1781) argued that claims about “being as such” outstrip what reason can justify. Kant’s critique relegated Wolffian ontology to a status of historical interest—undeniably part of the tradition, but not beyond reproach.

In the 19th century, positivists dismissed metaphysical speculation, leaving ontology in eclipse. Yet its revival came from two directions. Edmund Husserl introduced formal ontology in his Logical Investigations (1900), mapping universal categories such as part–whole and dependence structures that underlie any domain of being. Around the same time, his student, Martin Heidegger called for a fundamental ontology in Being and Time (1927), an existential analysis of Dasein to reveal the conditions that make any understanding of being possible, and distinguishing ontic facts from ontological structures. Simultaneously, in the analytic tradition, W. V. O. Quine declared in 1948 that determining “what exists”—ontology—is a central philosophical task, famously asserting, “To be is to be the value of a variable.” These developments restored ontology to prominence across both Continental and analytic philosophies.

By the 1980s, AI researchers confronted a practical problem: how to make software reason about domains consistently. They seized on ontology to name their solution. In 1993, Tom Gruber defined an ontology as an explicit specification of a conceptualization, acknowledging the term’s philosophical heritage and recasting it as a technical artifact. The Semantic Web cemented this shift: with the OWL Web Ontology Language (2004), data on the internet could carry formal definitions of classes, properties, and relationships, enabling different systems to interoperate semantically. Ontologies now powered knowledge graphs, expert systems, and data integration projects across industries.

Today, Palantir Technologies—under CEO Alexander Karp, a scholar of the Frankfurt School—instituted “Ontology” as the beating heart of its Foundry platform. Palantir’s ontology is a dynamic knowledge model: it maps raw datasets to object types (e.g., Customer, Asset), link types (e.g., owns, supplied_by), and action types (e.g., transfer, inspect). In earnings calls and marketing materials, Karp emphasizes ontology as the company’s “secret sauce,” a remedy for data fragmentation, echoing Frankfurt School critiques of conceptual alienation, and as a shared semantic framework that restores a “grammar” to complex organizations.

The leap from Heidegger’s Being and Time to business dashboards may seem vast, but the core skills align. Philosophers learn to isolate the key thing in any context; in tech, this means defining domain entities like User, Session, or Transaction. Heidegger’s analysis of concernful dealings parallels the modeling of dependencies and workflows—permissions, data flows, and temporal sequences. Quine’s discipline of ontological commitment keeps schema sprawl in check by asking, Does our model really need that class? Husserl’s regional ontologies remind us that each domain carries its own nuances—healthcare, finance, or manufacturing all demand bespoke concept hierarchies. These capabilities form the basis of Ontology-as-a-Service: offering schema design, governance frameworks, and workshops so organizations can build robust knowledge architectures that underpin AI, analytics, and user experiences.

As AI systems become ubiquitous, the need for clear semantic foundations intensifies. LLMs misinterpret data without structured concepts guiding their reasoning; industries demand shared vocabularies to combine datasets across silos; ontologies can encode regulatory rules and access policies at the concept level rather than buried in code; and understanding the lifeworld (Lebenswelt) of users—a Heideggerian notion—improves UX by aligning system structures with human practices. Philosophical ontology offers a time-tested methodology for asking the right questions about what to model, why, and how those elements interlock.

I may not have been recruited with a LinkedIn headline of “Ontologist,” but I embrace the title. My route—from parsing Heidegger’s footnotes to running data modeling workshops—reveals a deep continuity: both pursuits aim to illuminate structures of meaning. The word ontology, coined by Lorhard in 1606, has proved astonishingly versatile. It has survived Kant’s critique, driven phenomenology, fueled analytic metaphysics, powered AI research, and now underwrites enterprise digital twins.

When I help thinkers map their knowledge, I stand on the shoulders of four centuries of thinkers. My doctorate in Heidegger gave me the tools to see the invisible scaffolding of concepts; the tech industry gave me a way to apply those tools. Philosophy contains within it an “embedded option.” For as technology progresses, so too grows the need to think.