Hey folks! Today, let’s dive into the intriguing world of neurosymbolic approaches, retrieval-augmented generation (RAG), and personal knowledge graphs (PKGs). Together, these concepts hold much potential for bringing true reasoning capabilities to large language models (LLMs). So, let’s break down how symbolic logic, knowledge graphs, and modern AI can come together to empower future AI systems to reason like humans.
## The Neurosymbolic Approach: What It Means ?
Neurosymbolic AI combines two historically separate streams of artificial intelligence: symbolic reasoning and neural networks. Symbolic AI uses formal logic to process knowledge, similar to how we might solve problems or deduce information. On the other hand, neural networks, like those underlying GPT-4, focus on learning patterns from vast amounts of data — they are probabilistic statistical models that excel in generating human-like language and recognizing patterns but often lack deep, explicit reasoning.
While GPT-4 can produce impressive text, it’s still not very effective at reasoning in a truly logical way. Its foundation, transformers, allows it to excel in pattern recognition, but the models struggle with reasoning because, at their core, they rely on statistical probabilities rather than true symbolic logic. This is where neurosymbolic methods and knowledge graphs come in.
## Symbolic Calculations and the Early Vision of AI
If we take a step back to the 1950s, the vision for artificial intelligence was very different. Early AI research was all about symbolic reasoning — where computers could perform logical calculations to derive new knowledge from a given set of rules and facts. Languages like **Lisp** emerged to support this vision, enabling programs to represent data and code as interchangeable symbols. Lisp was designed to be homoiconic, meaning it treated code as manipulatable data, making it capable of self-modification — a huge leap towards AI systems that could, in theory, understand and modify their own operations.
## Lisp: The Earlier AI-Language
**Lisp**, short for “LISt Processor,” was developed by John McCarthy in 1958, and it became the cornerstone of early AI research. Lisp’s power lay in its flexibility and its use of symbolic expressions, which allowed developers to create programs that could manipulate symbols in ways that were very close to human reasoning. One of the most groundbreaking features of Lisp was its ability to treat code as data, known as homoiconicity, which meant that Lisp programs could introspect and transform themselves dynamically. This ability to adapt and modify its own structure gave Lisp an edge in tasks that required a form of self-awareness, which was key in the early days of AI when researchers were exploring what it meant for machines to “think.”
Lisp was not just a programming language—it represented the vision for artificial intelligence, where machines could evolve their understanding and rewrite their own programming. This idea formed the conceptual basis for many of the self-modifying and adaptive algorithms that are still explored today in AI research. Despite its decline in mainstream programming, Lisp’s influence can still be seen in the concepts used in modern machine learning and symbolic AI approaches.
## Prolog: Formal Logic and Deductive Reasoning
In the 1970s, **Prolog** was developed—a language focused on formal logic and deductive reasoning. Unlike Lisp, based on lambda calculus, Prolog operates on formal logic rules, allowing it to perform deductive reasoning and solve logical puzzles. This made Prolog an ideal candidate for expert systems that needed to follow a sequence of logical steps, such as medical diagnostics or strategic planning.
Prolog, like Lisp, allowed symbols to be represented, understood, and used in calculations, creating another homoiconic language that allows reasoning. Prolog’s strength lies in its rule-based structure, which is well-suited for tasks that require logical inference and backtracking. These features made it a powerful tool for expert systems and AI research in the 1970s and 1980s.
The language is declarative in nature, meaning that you define the problem, and Prolog figures out **how** to solve it. By using formal logic and setting constraints, Prolog systems can derive conclusions from known facts, making it highly effective in fields requiring explicit logical frameworks, such as legal reasoning, diagnostics, and natural language understanding. These symbolic approaches were later overshadowed during the AI winter — but the ideas never really disappeared. They just evolved.
## Solvers and Their Role in Complementing LLMs
One of the most powerful features of **Prolog** and similar logic-based systems is their use of **solvers**. Solvers are mechanisms that can take a set of rules and constraints and automatically find solutions that satisfy these conditions. This capability is incredibly useful when combined with LLMs, which excel at generating human-like language but need help with logical consistency and structured reasoning.
For instance, imagine a scenario where an LLM needs to answer a question involving multiple logical steps or a complex query that requires deducing facts from various pieces of information. In this case, a **solver** can derive valid conclusions based on a given set of logical rules, providing structured answers that the LLM can then articulate in natural language. This allows the LLM to retrieve information and ensure the logical integrity of its responses, leading to much more robust answers.
Solvers are also ideal for handling **constraint satisfaction problems** — situations where multiple conditions must be met simultaneously. In practical applications, this could include scheduling tasks, generating optimal recommendations, or even diagnosing issues where a set of symptoms must match possible diagnoses. Prolog’s solver capabilities and LLM’s natural language processing power can make these systems highly effective at providing intelligent, rule-compliant responses that traditional LLMs would struggle to produce alone.
By integrating **neurosymbolic methods** that utilize solvers, we can provide LLMs with a form of deductive reasoning that is missing from pure deep-learning approaches. This combination has the potential to significantly improve the quality of outputs for use-cases that require explicit, structured problem-solving, from legal queries to scientific research and beyond. Solvers give LLMs the backbone they need to not just generate answers but to do so in a way that respects logical rigor and complex constraints.
## Graph of Rules for Enhanced Reasoning
Another powerful concept that complements LLMs is using a **graph of rules**. A graph of rules is essentially a structured collection of logical rules that interconnect in a network-like structure, defining how various entities and their relationships interact. This structured network allows for complex reasoning and information retrieval, as well as the ability to model intricate relationships between different pieces of knowledge.
In a **graph of rules**, each node represents a rule, and the edges define relationships between those rules — such as dependencies or causal links. This structure can be used to enhance LLM capabilities by providing them with a formal set of rules and relationships to follow, which improves logical consistency and reasoning depth. When an LLM encounters a problem or a question that requires multiple logical steps, it can traverse this graph of rules to generate an answer that is not only linguistically fluent but also logically robust.
For example, in a healthcare application, a graph of rules might include nodes for medical symptoms, possible diagnoses, and recommended treatments. When an LLM receives a query regarding a patient’s symptoms, it can use the graph to traverse from symptoms to potential diagnoses and then to treatment options, ensuring that the response is coherent and medically sound. The graph of rules guides reasoning, enabling LLMs to handle complex, multi-step questions that involve chains of reasoning, rather than merely generating surface-level responses.
Graphs of rules also enable **modular reasoning**, where different sets of rules can be activated based on the context or the type of question being asked. This modularity is crucial for creating adaptive AI systems that can apply specific sets of logical frameworks to distinct problem domains, thereby greatly enhancing their versatility. The combination of **neural fluency** with **rule-based structure** gives LLMs the ability to conduct more advanced reasoning, ultimately making them more reliable and effective in domains where accuracy and logical consistency are critical.
By implementing a graph of rules, LLMs are empowered to perform **deductive reasoning** alongside their generative capabilities, creating responses that are not only compelling but also logically aligned with the structured knowledge available in the system. This further enhances their potential applications in fields such as law, engineering, finance, and scientific research — domains where logical consistency is as important as linguistic coherence.
## Enhancing LLMs with Symbolic Reasoning
Now, with LLMs like GPT-4 being mainstream, there is an emerging need to add real reasoning capabilities to them. This is where **neurosymbolic approaches** shine. Instead of pitting neural networks against symbolic reasoning, these methods combine the best of both worlds. The neural aspect provides language fluency and recognition of complex patterns, while the symbolic side offers real reasoning power through formal logic and rule-based frameworks.
**Personal Knowledge Graphs (PKGs)** come into play here as well. Knowledge graphs are data structures that encode entities and their relationships — they’re essentially semantic networks that allow for structured information retrieval. When integrated with neurosymbolic approaches, LLMs can use these graphs to answer questions in a far more contextual and precise way. By retrieving relevant information from a knowledge graph, they can ground their responses in well-defined relationships, thus improving both the relevance and the logical consistency of their answers.
Imagine combining an LLM with a **graph of rules** that allow it to reason through the relationships encoded in a personal knowledge graph. This could involve using **deductive databases** to form a sophisticated way to represent and reason with symbolic data — essentially constructing a powerful hybrid system that uses LLM capabilities for language fluency and rule-based logic for structured problem-solving.
## My Research on Deductive Databases and Knowledge Graphs
I recently did some research on modeling **knowledge graphs using deductive databases**, such as DataLog — which can be thought of as a limited, data-oriented version of Prolog. What I’ve found is that it’s possible to use formal logic to model knowledge graphs, ontologies, and complex relationships elegantly as rules in a deductive system. Unlike classical RDF or traditional ontology-based models, which sometimes struggle with complex or evolving relationships, a deductive approach is more flexible and can easily support dynamic rules and reasoning.
**Prolog** and similar logic-driven frameworks can complement LLMs by handling the parts of reasoning where explicit rule-following is required. LLMs can benefit from these rule-based systems for tasks like entity recognition, logical inferences, and constructing or traversing knowledge graphs. We can even create a **graph of rules** that governs how relationships are formed or how logical deductions can be performed.
The future is really about creating an AI that is capable of both deep contextual understanding (using the powerful generative capacity of LLMs) and true reasoning (through symbolic systems and knowledge graphs). With the neurosymbolic approach, these AIs could be equipped not just to generate information but to explain their reasoning, form logical conclusions, and even improve their own understanding over time — getting us a step closer to true artificial general intelligence.
## Why It Matters for LLM Employment
Using **neurosymbolic RAG (retrieval-augmented generation)** in conjunction with personal knowledge graphs could revolutionize how LLMs work in real-world applications. Imagine an LLM that understands not just language but also the relationships between different concepts — one that can navigate, reason, and explain complex knowledge domains by actively engaging with a personalized set of facts and rules.
This could lead to practical applications in areas like healthcare, finance, legal reasoning, or even personal productivity — where LLMs can help users solve complex problems logically, providing relevant information and well-justified reasoning paths. The combination of **neural fluency** with **symbolic accuracy and deductive power** is precisely the bridge we need to move beyond purely predictive AI to truly intelligent systems.
Let's explore these ideas further if you’re as fascinated by this as I am. Feel free to reach out, follow my YouTube channel, or check out some articles I’ll link below. And if you’re working on anything in this field, I’d love to collaborate!
Until next time, folks. Stay curious, and keep pushing the boundaries of AI!