Semantic Reasoning with LLMs

Enhancing Language Models with Ontological Knowledge

This guide explores how to enhance Large Language Models (LLMs) with semantic reasoning capabilities using ontologies, with a focus on plant disease diagnosis applications.

Why Semantic Reasoning in LLMs?

The Knowledge Gap in LLMs

Traditional LLMs lack:

  • Structured knowledge about domain-specific relationships
  • Consistent reasoning based on formal logic
  • Explainable decisions grounded in domain knowledge

The Ontology Advantage

graph LR
    A[LLM] --> B{Ontology}
    B --> C[Structured Knowledge]
    B --> D[Formal Reasoning]
    B --> E[Domain-Specific Constraints]
    C --> F[More Accurate Outputs]
    D --> F
    E --> F

Core Components

1. Knowledge Graph Integration

from SPARQLWrapper import SPARQLWrapper, JSON

def query_plant_diseases(symptom: str) -> list:
    """Query plant diseases from GraphDB based on symptoms."""
    sparql = SPARQLWrapper("http://localhost:7200/repositories/plant-ontology")
    query = """
    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    PREFIX plant: <http://example.org/plant-ontology#>
    
    SELECT ?disease ?description ?treatment
    WHERE {
        ?disease rdf:type plant:Disease ;
                 plant:hasSymptom ?symptom ;
                 plant:description ?description ;
                 plant:hasTreatment ?treatment .
        ?symptom plant:name "%s" .
    }
    """ % symptom
    
    sparql.setQuery(query)
    sparql.setReturnFormat(JSON)
    results = sparql.query().convert()
    return results["results"]["bindings"]

2. Prompt Engineering with Semantic Context

class OntologyPromptEnhancer:
    def __init__(self, ontology_endpoint: str):
        self.endpoint = ontology_endpoint
        
    def enhance_prompt(self, user_query: str) -> str:
        """Enhance prompt with relevant ontological context."""
        # Extract key concepts
        concepts = self.extract_concepts(user_query)
        
        # Query ontology for relationships
        context = self.query_ontology_context(concepts)
        
        # Construct enhanced prompt
        return f"""You are a plant pathology expert with access to formal knowledge.
        
        Ontological Context:
        {context}
        
        User Query: {user_query}
        
        Provide a detailed response based on the above context and your training."""

Implementation Patterns

1. Retrieval-Augmented Generation (RAG) with Ontologies

sequenceDiagram
    participant User
    participant LLM
    participant Ontology
    participant VectorDB
    
    User->>LLM: Query about plant disease
    LLM->>VectorDB: Semantic search
    VectorDB-->>LLM: Relevant chunks
    LLM->>Ontology: Query relationships
    Ontology-->>LLM: Structured knowledge
    LLM-->>User: Informed response

2. Fine-tuning with Ontological Constraints

from transformers import Trainer, TrainingArguments

def ontology_aware_loss(model, inputs, return_outputs=False):
    """Custom loss function incorporating ontological constraints."""
    outputs = model(**inputs)
    logits = outputs.get("logits")
    
    # Standard cross-entropy loss
    loss_fct = torch.nn.CrossEntropyLoss()
    loss = loss_fct(logits.view(-1, model.config.num_labels), inputs["labels"].view(-1))
    
    # Add ontological constraint loss
    ontology_loss = compute_ontology_violation_loss(model, inputs)
    
    total_loss = loss + 0.1 * ontology_loss  # Weighted combination
    return (total_loss, outputs) if return_outputs else total_loss

# Usage in training
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=8,
    num_train_epochs=3,
    save_steps=10_000,
    save_total_limit=2,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_loss=ontology_aware_loss,  # Custom loss function
)

Case Study: Plant Disease Diagnosis

1. Symptom to Disease Mapping

def diagnose_plant_disease(symptoms: List[str], confidence_threshold: float = 0.7):
    """Diagnose plant disease based on symptoms using LLM and ontology."""
    # Query ontology for potential diseases
    potential_diseases = query_ontology_for_diseases(symptoms)
    
    # Generate LLM prompt with context
    prompt = create_diagnosis_prompt(symptoms, potential_diseases)
    
    # Get LLM response
    response = generate_with_llm(prompt)
    
    # Validate against ontology constraints
    validated_response = validate_with_ontology(response)
    
    # Calculate confidence score
    confidence = calculate_confidence(validated_response, symptoms)
    
    if confidence < confidence_threshold:
        return {"diagnosis": "Inconclusive", 
                "confidence": confidence,
                "suggested_actions": ["Provide more symptoms", "Consult an expert"]}
                
    return {
        "diagnosis": validated_response["disease"],
        "confidence": confidence,
        "treatment": validated_response["treatment"],
        "prevention": validated_response["prevention"]
    }

Best Practices

1. Ontology Design

  • Use standardized ontologies (e.g., Plant Ontology, Plant Disease Ontology)
  • Define clear relationships between concepts
  • Include domain-specific constraints and rules

2. Model Integration

  • Use ontologies for prompt engineering
  • Implement semantic validation of model outputs
  • Combine with vector databases for hybrid retrieval

3. Evaluation

  • Measure factual accuracy against ground truth
  • Assess consistency with ontological constraints
  • Monitor for hallucination rates

Next Steps

  1. Set up GraphDB with plant disease ontologies
  2. Implement the RAG pipeline with ontology integration
  3. Fine-tune LLMs with ontological constraints
  4. Deploy as a diagnostic assistant

References