Large Language Models (LLMs) & Knowledge Graph Symbiosis — Revisited

Despite the hype and euphoria associated with Large Language Models (LLMs), it's important to put them in practical perspective.

As Translators

LLMs excel at translating between languages—whether natural (e.g., English to Spanish) or artificial (e.g., one programming language to another). This strength stems from their ability to recognize statistical patterns across vast corpora of text.

Translation, at its core, is about mapping patterns from one language system to another while preserving meaning and adapting to the syntax and conventions of the target language. LLMs are particularly effective here because they specialize in syntactic manipulation—not necessarily semantic understanding.

In short, they are very good at handling syntax, but that doesn’t mean they are equally good at handling semantics.

Language is a system of signs, syntax, and semantics used to encode and decode information (i.e., data within a context). It should never be conflated with knowledge.

Educated Guessing (Abduction)

Abduction is a form of reasoning that generates explanatory hypotheses from observations. Unlike:

• Deduction, which derives guaranteed conclusions from premises, or

• Induction, which generalizes from specific cases,

Abduction involves generating plausible hypotheses to explain what’s observed.

LLMs are particularly good at abduction because they:

• Recognize patterns in large datasets

• Generate plausible continuations

• Infer missing information based on context

This enables them to:

• Complete partial inputs

• Suggest reasonable explanations

• Create imaginative content

• Predict outcomes from incomplete data

The Hallucination Problem

The same abductive mechanism that powers LLMs’ creativity also produces hallucinations. Since LLMs generate statistically likely outputs—not verified truths—they can produce:

• Plausible-sounding but factually incorrect information

• Coherent references that don’t exist

• Logical-looking but invalid arguments

These hallucinations aren’t bugs—they’re artifacts of how LLMs are trained to guess what “sounds right” rather than what is right.

Reframing LLM Value

Once we get past the noise, we’re left with something incredibly valuable: a transformative step forward in natural language-based UI/UX for computing.

LLMs unshackle both end-users and developers from the constraints of traditional Command-Line Interfaces (CLIs) and Graphical User Interfaces (GUIs), making computing more accessible and productive through language-first interaction.

What’s Next: Symbiosis with Symbolic Reasoning

The next major leap lies in combining this new UI/UX paradigm with verifiable reasoning and inference—specifically by linking LLMs with Knowledge Graphs (the contemporary moniker for the Semantic Web vision).

This means recognizing the symbiotic relationship between:

• Natural Language Processing (NLP), as enabled by LLMs, and

• Symbolic Logic woven into the Internet, via a Web of Data, as embodied in the Semantic Web Project’s core vision

The notion of a "Semantic Web" has always been fundamentally about representing knowledge in machine-computable form via ontologies, symbolic relationships, and globally connected entity relationship graphs built atop Internet connectivity. When fused with LLM-powered language interfaces, it brings us closer to building AI Agents and Agentic workflows that are both expressive and trustworthy.