Table of Contents
- Distilling Ontologies from FMs is “Magic”
- Formalizing their Latent Knowledge
- The Fallacy of "Real" Ontologies
- The Statistical Ontology Advantage
- Symbolic Knowledge Distillation: What We're Doing
- Deep Dive: The Distillation Pipeline
- Real-World Performance Metrics
- Why This Matters
- The Enterprise Reality Check
- Beyond Static Ontologies are Living Knowledge Systems
- The Bridge Between Symbolic and Statistical
- The Neurosymbolic Convergence
- The Competitive Advantage
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Distilling Ontologies from FMs is “Magic”
There's a truth that traditional ontology communities are reluctant to face: Large language models already contain world models. They're not formally axiomatized. They're not neat. They're not hand-built by committees. But they work.
If you've ever asked an LLM to compare political philosophies, summarize a research paper, or explain the concept of "customer lifetime value" across different industries, you've used its latent world model. That model—compressed, emergent, and statistical—isn't a formal ontology. But for many use cases, it's good enough. For many use cases, suitably tickled, it can be made to emit a formal ontology.
So the question isn't whether LLMs "have" ontologies. The question is what we can do with the (many) ones they already contain. It’s not “can they conceptualize a domain” but, rather, how do we algorithmically neaten their polyvalent scruff?
Formalizing their Latent Knowledge
Recent research in mechanistic interpretability has revealed something remarkable: transformer architectures naturally develop hierarchical representations that mirror symbolic knowledge structures. Studies by Anthropic and others show that LLMs “naturally” form concept clusters, causal chains, and even rudimentary logical inference patterns during training.
Sparse autoencoders have improved our understanding of how neural networks represent knowledge internally. When we probe GPT-4's internal representations for concepts like "mammal" or "CEO," we find dense, structured embeddings that encode not just semantic similarity but relational knowledge: the kind of taxonomic and part-whole relationships that human ontologists spend years manually, formally encoding. Life is too short, man!
The breakthrough work on mechanistic interpretability demonstrates that we can identify computational circuits within transformers that implement structured reasoning. Recent advances in scaling sparse autoencoders to models like GPT-4 show we can decompose neural activations into millions of interpretable features.
We can build it. We have the technology.
The Fallacy of "Real" Ontologies
Traditionalists will object: "That's not a real ontology. A true ontology is a formal conceptualization of a domain. No brain, no mind, no conceptualization, etc.”
But that's just the No True Scotsman fallacy in ontological drag. LLMs aren't limited by a single formal conceptualization. They contain many, overlapping ones learned from books, technical manuals, Reddit threads, and scientific ontologies alike. The knowledge is fuzzy, redundant, and sometimes contradictory. Scruffy AF!
But it's there. And it can be tapped.
The Statistical Ontology Advantage
Here's what traditional ontology builders miss (because Upton Sinclair?): coverage beats perfection. So does automation. A manually curated biomedical ontology might have 50,000 precisely defined concepts. But GPT-4's latent biomedical knowledge spans millions of entities, relationships, and contextual nuances learned from the entirety of medical literature, not just what made it into formal standards.
The statistical nature isn't a bug; it's a feature. Real-world knowledge is inherently probabilistic, contextual, and contradictory. A CEO might be both a "person" and a "role" depending on the query context. Traditional ontologies force artificial binary distinctions. LLMs embrace the suck of ambiguity because that’s what the Python code tells them to do because we don’t know (yet or ever?) how to learn a model in another way.
Symbolic Knowledge Distillation: What We're Doing
At Stardog, we're working on symbolic knowledge distillation:
the extraction of formal, machine-verifiable ontologies from the latent world knowledge inside a foundation model.
This isn't a thought experiment. It's a practical, multi-phase process:
- Prompt scaffolding to coax out latent structures and semantic relationships including critically Competency Questions.
- Symbolic alignment with existing ontologies and controlled vocabularies largely in order to increase the surface area of steerability in domain settings (i.e., our customers).
- Formal encoding into OWL, SHACL, or other knowledge representation languages when and as needed to power symbolic tooling.
- Iterative validation using Stardog's reasoning and inference stack.
The output isn't just "text" that looks like an ontology. We don’t cosplay this shit. It's real, logical, queryable symbolic structure with provenance, lineage, and testability.
Deep Dive: The Distillation Pipeline
Our distillation process leverages several breakthrough techniques from recent research.
Concept Probing with Structured Queries: We use carefully crafted prompts that mirror formal logic patterns. "What are the necessary and sufficient conditions for X?" or "Which properties of Y are inherited by all instances of Z?" This isn't just asking the LLM to generate ontology-like text; it's systematically probing the model's internal concept representations using techniques pioneered in symbolic knowledge distillation research.
Consistency Validation Through Logical Inference: Every extracted relationship undergoes automated consistency checking using Stardog's reasoning engine. If the LLM suggests that "all executives are employees" but also "some executives are contractors," our validation pipeline catches and resolves these inconsistencies (if there is in fact a constraint violation) through iterative refinement, building on dual-system neuro-symbolic approaches for logical consistency.
Confidence-Weighted Knowledge Extraction: Not all LLM outputs are equally reliable. We've developed techniques to assess the confidence of extracted knowledge based on response consistency across multiple prompts, internal attention patterns, and alignment with existing validated knowledge bases. This draws from recent work on understanding neural network feature representations and interpretable feature discovery.
Real-World Performance Metrics
In pilot deployments, our distilled ontologies achieve…just kidding, we aren’t that advanced just yet to publicize metrics but we will ship early and often. In fact, the first downpayment on all of this drops in our September, 2025, release with basic ontologies emitted from a GenAI multi-agent system in Voicebox.
Why This Matters
Enterprise AI today is bottlenecked by a lack of portable machine-legible biz context: models hallucinate, agents get stuck, and answers lack grounding.
You don't fix that with bigger models or more RAG. You fix it by giving AI systems access to structured, explainable knowledge.
The catch? Manually building formal ontologies is slow, expensive, and brittle.
Distilling them from LLMs is faster, cheaper, and more scalable, especially when combined with human-machine teaming workflows and knowledge graph infrastructure.
The business benefits are compelling: faster time-to-knowledge for new domains, which means people and agents get the answers they need faster than ever; automated alignment of language and semantics, which means people and agents can trust the answers they get; and, ontologies that evolve with language, not in spite of it, which means these ontologies are cheap to create and maintain so that scalability can happen.
The Enterprise Reality Check
Consider the pharmaceutical industry. Traditional ontology development for a new therapeutic area takes 18-24 months and costs… who knows, really, but it aint cheap! Expert committees debate whether "drug resistance" is a process, a quality, or a disposition. That’s the costliest bit, frankly, and just a real unending soul suck for morale and sense of urgency.
Meanwhile, LLMs already encode nuanced understanding of drug resistance mechanisms, biomarkers, and clinical implications that were learned from processing the entire corpus of medical literature.
Our distillation approach will produce a working therapeutic ontology in hours, not years. It won't replace expert curation entirely, but it provides a sophisticated starting point that captures 90% of relevant domain knowledge automatically.
Beyond Static Ontologies are Living Knowledge Systems
The most exciting opportunity isn't just faster ontology development but it's also dynamic ontologies that evolve with new knowledge. As new research emerges or business contexts shift, our distillation pipelines can continuously update and extend knowledge representations using iterative prompting approaches and automated knowledge graph construction.
Traditional ontologies become outdated the moment they're published. In fairness so do LLMs! But massive resources are being poured into the latter while the former is a marginal academic exercise at best.
"You will not find it difficult to prove that battles, campaigns, and even wars have been won or lost primarily because of logistics."—General Eisenhower
Distilled ontologies can incorporate new information as it becomes available, maintaining both formal structure and contemporary relevance.
The Bridge Between Symbolic and Statistical
We're not saying LLMs replace ontologies. We're saying they seed them.
They offer raw material for a new kind of hybrid knowledge system where statistical inference meets symbolic structure. Where the fuzziness of language is channeled into the machine-like rigor of logic. And where enterprise AI can really reason, explain, and trust at scale.
The Neurosymbolic Convergence
This work sits at the intersection of several converging research trends.
Mechanistic Interpretability: Understanding what LLMs learn and how they represent knowledge internally, building on foundational work in transformer circuit discovery and sparse autoencoder scaling.
Neurosymbolic AI: Combining the strengths of neural and symbolic approaches to AI, as outlined in comprehensive surveys of neurosymbolic computing and neural-symbolic learning systems.
Knowledge Graph Embeddings: Bridging statistical and structural representations of knowledge through neural-symbolic reasoning on knowledge graphs and graph neural network integration.
Prompt Engineering as Programming: Treating natural language prompts as a form of knowledge elicitation code, using structured prompting techniques and chain-of-thought reasoning.
If we were Cool Kids, I’d say “we're pioneering a new paradigm where the boundary between learned and engineered knowledge becomes productively blurred.”
But nah twin we aint about all that. We’re about building the best possible real-world product as fast as humanly possible.
The Competitive Advantage
Organizations that master symbolic knowledge distillation will have a fundamental advantage in the AI-driven economy. They'll be able to
- Rapidly capture institutional knowledge before experts retire or leave
- Scale domain expertise across multiple business units and use cases
- Maintain explainable AI systems that meet regulatory and compliance requirements using machine-assisted verification
- Adapt quickly to new domains without starting knowledge modeling from scratch
The future isn't one or the other. It's both and Stardog is building the bridge.
