Enhancing Knowledge Extraction with a Cellular Automata Graph Chunker & Retriever – A Work in Progress

In today’s information-driven world, extracting precise and context-rich knowledge from complex documents is more than a technical challenge—it’s a competitive necessity. Over the past several months, I have been developing a Cellular Automata Graph Chunker and Retriever that rethinks document processing and retrieval. By constructing a dynamic knowledge graph and leveraging cellular automata (CA) with fuzzy logic, I am building a system that refines segmentation and contextual understanding far beyond traditional methods. This article delves into the details of my approach, how I plan to enhance accuracy, and the next steps on this exciting journey.

A Dynamic Knowledge Graph

My solution transforms traditional document processing by constructing a hierarchical knowledge graph where:

• Document Nodes serve as the root, representing entire texts.
• Context Nodes—derived from dynamic topic modeling—capture the core themes of a document.
• Subsequent Nodes represent paragraphs, sentences, and phrases that are connected to their respective context node.

This structure preserves the inherent relationships and context within a document, making retrieval much richer and more meaningful compared to typical vectordb approaches.

Cellular Automata: Breathing Life into the Graph

One of the most innovative aspects of my work is the integration of cellular automata into the knowledge graph. Unlike static vector similarity methods, my CA approach dynamically updates node states using fuzzy logic. Here’s how I implement it:

1. State Initialization: I first embed every text chunk (paragraphs, sentences, phrases) into a high-dimensional vector space using a SentenceTransformer. These embeddings serve as the initial state for each node.
2. Fuzzy Logic Updates: Instead of binary states, I employ a fuzzy logic-based sigmoid function to update node states. This produces a continuum (0 to 1) that captures nuanced relevance and influence. For example:

def fuzzy_update_state(current_state, influence_score, steepness=5.0):
    return expit(steepness * (current_state + influence_score - 0.5))}
  return go(f, seed, [])
}        

1. Iterative CA Updates Across Scales: I run the CA process across multiple scales (short, medium, long) based on an adaptive grid that categorizes nodes by word count. This iterative refinement captures both fine-grained details and broader contextual relationships:

def topic_model_paragraphs(paragraphs, min_topics=1, max_topics=5):
    # Process paragraphs, create corpus, and dynamically select optimal topics
    optimal_topics = select_optimal_num_topics(processed_paras, dictionary, corpus, min_topics, max_topics)
    lda_model = LdaModel(corpus, num_topics=optimal_topics, id2word=dictionary, passes=5, random_state=42)
    topic_labels = {topic: " ".join(word for word, _ in lda_model.show_topic(topic, topn=3))
                    for topic in range(optimal_topics)}
    return paragraph_topics, topic_labels, optimal_topics
        

This approach adds an extra layer of dynamic contextual refinement—one that static vectordb retrieval methods simply cannot match.

Dynamic Topic Modeling for Context Extraction

I use LDA-based topic modeling to extract the inherent themes from a document’s paragraphs dynamically. Instead of a fixed number of topics, I determine the optimal number using coherence measures. Each topic becomes a context node in the knowledge graph, with the node ID and content set to a human-readable topic label (for instance, “neural network deep” instead of “Topic 0”).

A simplified version of my dynamic topic modeling is:

def select_best_context_by_llm(query, context_candidates):
    prompt = "Given the query:\n\"{}\"\n\nand the following contexts:\n".format(query)
    for candidate in context_candidates:
        prompt += "- {}: {}\n".format(candidate["id"], candidate["content"])
    prompt += "\nWhich context is most relevant? Return only the context ID."
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=30,
        temperature=0
    )
    return response.choices[0].message["content"].strip()
        

This dynamic approach allows the context nodes to accurately reflect each document’s unique themes.

LLM-Enhanced Contextual Retrieval

The retrieval system in my solution goes beyond simple vector similarity search. It leverages a large language model (LLM) to interpret the query context and select the most relevant context node from the knowledge graph. Here’s a brief overview:

1. LLM-Assisted Context Selection: When a query is posed, I embed it and retrieve all context nodes. I then construct a prompt that lists the context candidates, and ask the LLM to return the most relevant context ID:
2. Subgraph Extraction: Once the best context node is selected, I retrieve its entire subgraph—delivering a complete and contextually rich segment of the document.

This multi-layered retrieval process ensures that the response is not only semantically similar but also highly relevant in context—a marked improvement over traditional vectordb approaches.

Enhancements to Increase Accuracy

I’m actively exploring several avenues to further enhance the accuracy of this system:

• Enhanced LLM Prompts and Re-ranking: I plan to refine LLM prompts further to improve context selection. Additionally, an iterative re-ranking process using the LLM can help adjust the retrieved results based on nuanced query understanding.
• Integration of ANN Techniques: For scalability and speed, I intend to integrate approximate nearest neighbor (ANN) libraries (such as FAISS) for rapid vector search across a large number of nodes. This will not only increase retrieval speed but also improve accuracy by enabling more precise similarity calculations.
• Feedback Loops for Continuous Improvement: Implementing a feedback mechanism where user inputs help fine-tune both the CA updates and LLM prompts could lead to continuous performance enhancements.
• Domain-Specific Tuning: Customizing the text extraction, topic modeling, and CA parameters for specific domains (legal, medical, academic) can significantly improve the accuracy of knowledge extraction in those areas.
• Interactive Visualization Tools: Developing a dashboard to visualize the knowledge graph and interact with the retrieval process will provide valuable insights into how the system is performing and where accuracy improvements are needed.

Conclusion and Next Steps

The Cellular Automata Graph Chunker and Retriever represents a paradigm shift in document processing. By combining hierarchical segmentation, dynamic topic modeling, fuzzy cellular automata, and LLM-enhanced retrieval, I am creating a system that mirrors human-like contextual understanding. Although it is still a work in progress, the innovative approach already offers significant improvements over conventional vectordb retrieval systems.

I am continuously refining this solution and will be integrating advanced indexing techniques, enhanced LLM capabilities, and interactive visualization in the near future. Stay tuned for further updates, and I look forward to releasing the code repository on GitHub soon. Your feedback and insights are most welcome as I work to transform intelligent knowledge extraction.

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