What is Ontology?

Ask the Community
1

What exactly is an “Ontology”? I am still struggling to grasp its definitive concept. Is it simply a process of establishing relationships between existing data points within an information system, or is there a deeper underlying philosophy to it? I would love to share my thoughts and hear your perspectives on this.

1. My definition of Ontology I view an ontology as a framework that reconstructs existing data into immutable, universally applicable “Objects,” and then maps out the diverse perspectives of various stakeholders onto them. I would appreciate hearing your thoughts on this definition.

2. What is the ideal approach to building an Ontology? My core question is this: Is it truly possible to create a reusable ontology that multiple users can utilize simultaneously? Currently, I find myself building bespoke ontologies tailored to specific use cases for each project, which feels like an anti-pattern. As a better alternative, I am considering an approach where we establish a core, immutable concept of the ontology for the organization first, and then derive various relationships from it. I would love to hear any real-world case studies or experiences regarding this.

3. Why Palantir Foundry? Why must it be Foundry? If you have built such systems within Foundry, what specific advantages or benefits have you experienced?

4. The Value of Ontology in the AI Era I believe ontologies will become increasingly critical in the age of AI. However, I see clear limitations in how our organization currently implements them. Right now, we simply ingest data from existing legacy systems to build the ontology, and any direct data mutations occur solely within those source systems. Foundry is merely used for tracking the current state. I am curious about how other organizations architect this workflow.

Thank you for reading my lengthy reflection. As a junior developer just starting this journey, I would deeply appreciate any advice or guidance you can share.

2

I love the questions.

I am still “young” in my Foundry Developer Journey too yet I have 3 thoughts:

A) The Ontology is only 1 part of what makes Palantir Foundry DOPE

  • Microsoft launched their version of the Ontology.
  • This scared me for a 1/2 second and made me step back and realize all the other dozens of features that makes foundry so powerful (i.e. access controls, built on Apollo, K-LLM, the list goes on and on.)

B) Real world building

  • The best way to learn is to get thrown into it.
  • You might have to get scrappy (i.e. build demos for local businesses on the free dev tier etc)
  • building for real customers helped me demystify “the ontology” lots!

C) HMU if you want to chat more!

  • fdejoe123@gmail.com
3

Agreed, but the key question is:

what is the difference between an Ontology and a database, data warehouse, or enterprise data lake that also supports automation and advanced reporting?

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Hey there Joman, I like to think of the ontology as a single pane of glass through which the organization can take actions on objects. Palantir is the platform that manages these objects, properties, link types, metadata, functions, & data connections to then build AI services on top of. a single source of truth that everyone in the organization can see. This single source of truth allows for faster iterations, improved communications and better orientations for success. I think for teams it’s a good idea to plan build principles, ontology foundation and doctrines, but then just run through the logic & build as many times as it takes to get it down good. Palantir’s suite allows each person to provide a unique perspective on the ontology by creating branches and utilizing their own set of tools for data synthesis.

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one ontology can have many data points

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Yes, Microsoft is a serious challenger. They are already deeply embedded in almost every enterprise environment, they have enormous distribution power, and they can adjust pricing in ways that make adoption easier for existing customers.

However, we are still here in the Palantir community because Palantir has its own “magic”: unique operational philosophy and a platform culture that many of us believe is hard to replicate.

What do you think?

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Thanks for sharing your thoughts! You are absolutely right—Foundry has so many powerful strengths beyond just the Ontology.

However, as Denis touched upon earlier, I am still looking for answers to a more fundamental question. Ultimately, it boils down to: “What truly is the Ontology, and how should we architect it?”

Most companies adopting Foundry likely already have established, in-house data platforms. In my view, what they expect from Foundry is the ability to integrate disparate data sources and transform them into a unified Ontology to drive better, data-driven decision-making.

From this perspective, advice like “just start building and you’ll eventually get a feel for the Ontology” doesn’t quite resonate with me. Simply configuring an Ontology to fit a single use case or to build a workshop demo feels insufficient.

That is why I want to dive deeper into the experience of senior developers who have been in the trenches. I would love to discuss:

  • How you actually integrate legacy data systems into a unified Ontology.

  • Why it must be the Ontology (the core value proposition).

  • The tangible benefits and advantages you have experienced firsthand through this architectural shift.

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What the Ontology truly is

The cleanest framing: the Ontology is a decision and action layer, not a data model. Traditional in-house platforms (warehouse, lake, semantic/metrics layer) model data for storage and query. The Ontology models the operational reality of the business — objects (nouns), links (relationships), actions (verbs that write back), and functions (logic) — so that decisions and operations can run on it directly.

The distinguishing feature is the kinetic element: actions that write back to source systems. A metrics layer or data catalog is read-only — it describes. The Ontology is closed-loop — you observe, decide, and act, and the act propagates. That’s the line between “another semantic layer” and the Ontology.

How you actually integrate legacy systems

You don’t rip and replace. The pattern:

  1. Pipelines ingest from each source (ERP, CRM, MES, support) into datasets — sources stay where they are.

  2. Datasets are mapped to object types via backing pipelines; links connect objects across sources.

  3. The “unified” part is entity resolution — the same customer in three systems collapses into one object. This is the hard, valuable part, not the ingestion.

The architectural shift: apps, automations, and AIP agents talk to objects, never to tables. The physical layer underneath can change without breaking anything above it.

Why it must be the Ontology

  • Decoupling. The decision layer is insulated from physical data. Swap a pipeline, the apps don’t break.

  • One definition. “Shipment,” “customer,” “order” are defined once and reused everywhere — kills the metric-drift problem where every team has its own version.

  • Write-back / closed loop. BI tells you what happened. The Ontology lets you do something about it and have it land in the system of record.

  • It’s the substrate AIP reasons over. Agents get typed, governed, permissioned access to the business — not raw tables.

  • Compounding reuse. The second and third use cases are cheap because the core objects already exist.

On your skepticism — you’re half right

“Just start building” is wrong if it means modeling to a single workshop demo. But “model the whole enterprise upfront” (waterfall ontology) is how these projects die. The disciplined middle: identify the durable core objects multiple decisions depend on, model those at the right grain, and let use cases accrete decoration around them. Start with a real decision — but pick a decision whose objects you’ll reuse. The “feel” senior devs talk about is mostly grain selection and what belongs in the shared core vs. the use-case edge.

The tangible payoff, stated plainly: simulate before you commit (branching), govern at the object level, and turn read-only analytics into operations that close the loop.

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AIP has microsoft tools; but micrsoft tools does not have AIP

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Loving these. Nobody stops to answer the meta-questions — everyone just gets straight to building.

My two cents:

The Ontology is the useful layer. It’s not the raw data — it’s what matters to end users and what they manipulate every day.

Your business shapes what your ontology looks like, just like your use cases do. If you’re a transportation company, you’ll need concepts like Hubs, Trucks, Routes — because that’s the nature of the work. The shape of your ontology is driven by your needs, not the other way around.

In the Palantir sense, the Ontology is a few things bundled together:

  • Object Types

  • Actions

  • Links

…and all of these are meant to be manipulated by end users.

Ontology vs. database: An Ontology sits on top of a database (for the objects part, at least), but it’s a user-centric one. It’s easily manipulable inside a Palantir

environment, and it comes with primitives like permissioning out of the box (if backed by datasets with markings or permissioned datasets).

▎ "Right now, we simply ingest data from existing legacy systems to build the ontology, and any direct data mutations occur solely within those source systems. Foundry is

▎ merely used for tracking the current state."

This is indeed not the typical approach — and it’s usually a failure pattern. Organizations that go this route tend to:

1. Pollute their ontology with unnecessary objects (everything from the source system gets dragged in).

2. Build permissions around the ontology too early — creating tiers of developers / gatekeeping at a stage where you should be building and shipping instead.

On the “ideal approach” question:

There’s no single right way. It depends on where you are in your Foundry journey. Are you a 1,000-user org or a <100-builder org?

If you’re small: build towards your use cases first. Don’t over-engineer. Run informal periodic reviews of your ontology internally to spot redundancies (the Palantir MCP makes this much easier now — huge unlock).

The ontology has to solve real problems and be operational first. The more traction you get, the more the business will reshape itself around your ontology. It’s partly a game of who can get the most traction.

Quick mental model on the alternatives / other mentions:

  • Data warehouse → where all the data lives, regardless of use. Unused? Warehouse. Raw? Warehouse. Enriched? Warehouse.

  • Enterprise data lake → not built for end-user application building. Yes, over time you can build apps on top — but at what cost? The engineering hoops and app-jumping in most contexts make it hard to justify leaving the platform.

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Hey there, Maveric! Great to meet you. Thank you for the detailed explanation; it was incredibly insightful.

It seems the fundamental difference between the Ontology and traditional data systems lies in their ultimate purpose. Rather than simply storing and managing data, the true value of the Ontology is its ability to mirror the real world through sophisticated modeling and to drive transformation through diverse ‘Actions.’

I also deeply resonate with your perspective on the methodology of building an Ontology. However, I have one lingering concern regarding the integration strategy.

If the data pipelines are not fully ingested into Foundry, I fear the role of ‘Actions’ becomes ambiguous, which could diminish the overall value of the Ontology. My reasoning is that if the actual decision-making and subsequent executions occur by manually updating data within legacy systems—rather than within Foundry itself—the ‘closed-loop’ benefit is lost.

In this regard, I am curious about your thoughts on the target architecture: Do you believe that after an initial implementation and a rigorous testing phase, Foundry should eventually become the primary interface for data entry and modification? Or do you envision a hybrid model where Foundry continues to coexist alongside legacy systems as a dual-track operation?

Lastly, I would love to hear thoughts and opinions from others on this topic. While our organization uses Foundry, we rarely have the chance to exchange such deep architectural views with a larger group. I believe engaging with a community like this is essential for broadening our perspective."

12

Great answers!

We can rephrase the question from “What is ontology?” to “Why Ontology?”

Imagine a large enterprise with a rich ecosystem of applications: CRM, ERP, data lake, reporting, workflow, and automation tools. These systems may be well designed and may work effectively. The company may not experience obvious problems, or may not recognize them as problems.

So the real question is not whether ontology is an interesting concept. The real question is whether the enterprise has a serious business reason to implement it.

Do they actually need Ontology? Who needs it? In what situations does Ontology become necessary? What problem does it solve that existing applications, databases, data lakes, and automation tools do not already solve?

In the real world, these tools are expensive and require serious commitment. A company needs a strong reason for implementation - not just curiosity or trend-following.

13

Hey Joman — great talking with you.

You’ve put your finger on exactly the right thing. Actions are where the ontology earns its keep. If the writes-back happen by hand in the legacy systems, what you’ve built is a very expensive mirror — it observes reality but doesn’t operate on it. That’s a dashboard, not a closed loop. The loop only closes when the decision and the execution both live where the data lives.

So to your question — primary interface or permanent hybrid — I’d actually frame it a third way: progressive absorption, not a forklift cutover and not dual-track forever.

Here’s how I think about it. The ontology is a digital twin, and Foundry is the toolkit for assembling and deploying one. Early on it sits over the legacy systems as the monitoring and orchestration layer — single pane of glass, read-first. You don’t rip anything out on day one. Then, system by system, you build the product inside Foundry, test it rigorously against the legacy behavior, and once it holds, you move the Action in and retire that piece of legacy. Hybrid is the transition state. Foundry-as-primary is the destination. You get there one proven loop at a time.

One hard-won caveat: don’t waterfall the ontology. It’s a living abstraction layer — it decays the moment you stop touching it, and a big-bang “design it all up front” approach degrades before you ever ship. I made that mistake early. You build it the same way you close the loops: bottom to top, incrementally, letting real use shape it.

Honestly, I think it’s this whole way of thinking — and the platforms that let you build ontologies this cleanly — is still in its infancy. If the Ontology is the single source of truth, then how are you as an organization going to get that? And be able to act with confidence that you’re all seeing the world the same way? I think this is where Palantir has been sandboxing for 20 years.

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I just started a job that uses Foundry too, and we work as consultants on top of that.

Part of our job is to explain Ontology as well, and although I have read the official definition from Palantir, this is still escaping me. Each of my senior has their own way to explain it, and I am not sure they know, but each of their explanation are different, based on their role.

  • Some people call it the digital twin - representation of objects within a business
  • Some call it the pre-joined data.
  • Others call it the abstraction layer that connect business logic to data
  • One person said it’s data in 2 formats, dataset and object type.

I’m still trying to figure it our myself. On the instinct level, I get what it is after using tools, building demos. And I can always repeat the mantra provided to me. But when helping a user trying to grasp it for the first time, it can be quite a challenge, and it can really depend on their expertise and experience with data.

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An ontology is what you get when you bind all those things together Some people call it the digital twin - representation of objects within a business

  • Some call it the pre-joined data.

  • Others call it the abstraction layer that connect business logic to data

  • One person said it’s data in 2 formats, dataset and object type. *coupled with the lived experiences, doctrine, and insights of the human operator.

Ontologies • Overview • Palantir- Why create an Ontology? • Palantir - Ontologies • Branching the ontology • Overview • Palantir