Ontology-Guided MoE with Attention

Combining Mixture of Experts with Ontologies and Attention

Overview

This approach combines the specialization benefits of Mixture of Experts (MoE) with the structured knowledge of ontologies and the focused processing power of attention mechanisms. Each expert specializes in specific ontological concepts while attention mechanisms guide focus to relevant image regions.

Core Architecture

A. Ontology-Structured Expert Assignment

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple, Optional

class OntologyGuidedRouter(nn.Module):
    """Routes inputs to experts based on ontological concept hierarchy"""
    
    def __init__(self, 
                 ontology_graph: Graph,
                 num_experts: int,
                 input_dim: int,
                 top_k: int = 2):
        super().__init__()
        
        self.ontology = ontology_graph
        self.num_experts = num_experts
        self.top_k = top_k
        
        # Build concept hierarchy from ontology
        self.concept_hierarchy = self._build_concept_hierarchy()
        
        # Concept embedding layer
        self.concept_embedder = nn.Embedding(
            len(self.concept_hierarchy), input_dim
        )
        
        # Expert assignment network
        self.router = nn.Sequential(
            nn.Linear(input_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(512, num_experts)
        )
        
        # Ontology-aware gating
        self.ontology_gate = nn.Sequential(
            nn.Linear(input_dim + len(self.concept_hierarchy), 256),
            nn.ReLU(),
            nn.Linear(256, num_experts)
        )
    
    def _build_concept_hierarchy(self) -> Dict[str, int]:
        """Extract concept hierarchy from ontology"""
        concepts = {}
        concept_id = 0
        
        # Query ontology for hierarchical concepts
        query = """
        SELECT DISTINCT ?concept ?parent WHERE {
            ?concept rdfs:subClassOf ?parent .
            FILTER(?concept != ?parent)
        }
        ORDER BY ?parent ?concept
        """
        
        results = self.ontology.query(query)
        for concept, parent in results:
            concept_name = str(concept).split('/')[-1]
            if concept_name not in concepts:
                concepts[concept_name] = concept_id
                concept_id += 1
        
        return concepts
    
    def forward(self, 
                features: torch.Tensor,
                concept_probs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            features: Input features [batch_size, feature_dim]
            concept_probs: Predicted concept probabilities [batch_size, num_concepts]
        
        Returns:
            expert_weights: Expert selection weights [batch_size, num_experts]
            expert_indices: Top-k expert indices [batch_size, top_k]
        """
        # Standard routing based on features
        routing_logits = self.router(features)
        
        # Ontology-aware routing
        concept_features = torch.matmul(concept_probs, self.concept_embedder.weight)
        combined_features = torch.cat([features, concept_probs], dim=-1)
        ontology_logits = self.ontology_gate(combined_features)
        
        # Combine routing strategies
        combined_logits = routing_logits + ontology_logits
        expert_weights = F.softmax(combined_logits, dim=-1)
        
        # Select top-k experts
        top_k_weights, top_k_indices = torch.topk(expert_weights, self.top_k)
        top_k_weights = top_k_weights / top_k_weights.sum(dim=-1, keepdim=True)
        
        return top_k_weights, top_k_indices

B. Concept-Specialized Experts

class ConceptSpecializedExpert(nn.Module):
    """Expert network specialized for specific ontological concepts"""
    
    def __init__(self, 
                 input_dim: int,
                 output_dim: int,
                 concept_id: int):
        super().__init__()
        self.concept_id = concept_id
        
        self.network = nn.Sequential(
            nn.Linear(input_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, output_dim)
        )
        
        # Concept-specific initialization
        self._initialize_for_concept()
    
    def _initialize_for_concept(self):
        """Initialize expert based on its concept"""
        # Custom initialization logic based on concept type
        for name, param in self.named_parameters():
            if 'weight' in name:
                nn.init.xavier_uniform_(param)
            elif 'bias' in name:
                nn.init.zeros_(param)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.network(x)

Training Strategy

A. Progressive Training

def train_ontology_guided_moe(model: nn.Module,
                            train_loader,
                            val_loader,
                            num_epochs: int = 100):
    """Progressive training strategy for ontology-guided MoE"""
    
    # Initialize optimizers and loss functions
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    criterion = nn.CrossEntropyLoss()
    
    # Learning rate scheduler
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
        optimizer, T_max=num_epochs
    )
    
    for epoch in range(num_epochs):
        model.train()
        total_loss = 0
        
        # Training loop
        for batch_idx, (images, targets) in enumerate(train_loader):
            images, targets = images.to(device), targets.to(device)
            
            # Forward pass
            outputs = model(images)
            
            # Calculate losses
            classification_loss = criterion(outputs['logits'], targets)
            
            # Expert load balancing loss
            load_balance_loss = model.calculate_load_balancing_loss()
            
            # Concept alignment loss
            concept_loss = model.calculate_concept_alignment_loss()
            
            # Total loss with regularization
            total_loss = (
                classification_loss + 
                0.1 * load_balance_loss +
                0.05 * concept_loss
            )
            
            # Backward pass and optimize
            optimizer.zero_grad()
            total_loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            
            if batch_idx % 100 == 0:
                print(f"Epoch [{epoch+1}/{num_epochs}], "
                      f"Step [{batch_idx}/{len(train_loader)}], "
                      f"Loss: {total_loss.item():.4f}")
        
        # Validation
        val_metrics = validate(model, val_loader)
        print(f"Epoch {epoch+1} Validation: {val_metrics}")
        
        # Update learning rate
        scheduler.step()

Visual Attention Mechanism

A. Ontology-Guided Attention

class OntologyGuidedVisualAttention(nn.Module):
    """Visual attention mechanism guided by ontological concepts"""
    
    def __init__(self, 
                 feature_dim: int,
                 num_concepts: int,
                 num_heads: int = 8):
        super().__init__()
        
        self.feature_dim = feature_dim
        self.num_heads = num_heads
        self.head_dim = feature_dim // num_heads
        
        # Concept projection layers
        self.concept_projections = nn.ModuleList([
            nn.Linear(feature_dim, feature_dim) 
            for _ in range(num_concepts)
        ])
        
        # Multi-head attention
        self.attention = nn.MultiheadAttention(
            embed_dim=feature_dim,
            num_heads=num_heads,
            batch_first=True
        )
        
        # Output projection
        self.output_proj = nn.Linear(feature_dim, feature_dim)
        
    def forward(self, 
                features: torch.Tensor,
                concept_probs: torch.Tensor) -> torch.Tensor:
        """
        Args:
            features: Input features [B, C, H, W]
            concept_probs: Concept probabilities [B, num_concepts]
            
        Returns:
            Attended features [B, C, H, W]
        """
        B, C, H, W = features.shape
        
        # Reshape features for attention
        spatial_features = features.flatten(2).transpose(1, 2)  # [B, H*W, C]
        
        # Generate concept-specific queries
        concept_queries = []
        for i, proj in enumerate(self.concept_projections):
            # Get concept-specific query
            concept_mask = concept_probs[:, i].unsqueeze(-1)  # [B, 1]
            concept_query = proj(spatial_features)  # [B, H*W, C]
            concept_queries.append(concept_query * concept_mask.unsqueeze(-1))
        
        # Combine concept queries
        query = torch.stack(concept_queries, dim=1)  # [B, num_concepts, H*W, C]
        query = query.mean(dim=1)  # Average concept queries
        
        # Apply multi-head attention
        attended, _ = self.attention(
            query=query,
            key=spatial_features,
            value=spatial_features
        )
        
        # Residual connection and output projection
        attended = self.output_proj(attended + query)
        
        # Reshape back to spatial dimensions
        attended = attended.transpose(1, 2).view(B, C, H, W)
        
        return attended

Complete Model Architecture

A. Ontology-Guided MoE Vision

class OntologyGuidedMoEVision(nn.Module):
    """Complete Ontology-Guided Mixture of Experts for Vision"""
    
    def __init__(self,
                 ontology_path: str,
                 num_classes: int,
                 num_experts: int = 8,
                 top_k: int = 2):
        super().__init__()
        
        # Load ontology
        self.ontology = self._load_ontology(ontology_path)
        
        # Feature extractor (e.g., ResNet backbone)
        self.feature_extractor = self._build_feature_extractor()
        feature_dim = 2048  # Output dimension of feature extractor
        
        # Concept predictor
        self.concept_predictor = nn.Sequential(
            nn.Linear(feature_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(512, len(self.concept_hierarchy)),
            nn.Sigmoid()
        )
        
        # Router
        self.router = OntologyGuidedRouter(
            ontology_graph=self.ontology,
            num_experts=num_experts,
            input_dim=feature_dim,
            top_k=top_k
        )
        
        # Experts
        self.experts = nn.ModuleList([
            ConceptSpecializedExpert(
                input_dim=feature_dim,
                output_dim=feature_dim,
                concept_id=i % len(self.concept_hierarchy)  # Distribute concepts
            ) for i in range(num_experts)
        ])
        
        # Attention mechanism
        self.attention = OntologyGuidedVisualAttention(
            feature_dim=feature_dim,
            num_concepts=len(self.concept_hierarchy)
        )
        
        # Classification head
        self.classifier = nn.Linear(feature_dim, num_classes)
    
    def _load_ontology(self, path: str) -> Graph:
        """Load and preprocess ontology"""
        # Implementation depends on ontology format
        pass
    
    def _build_feature_extractor(self) -> nn.Module:
        """Build CNN backbone"""
        # Implementation depends on the backbone architecture
        pass
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        # Extract features
        features = self.feature_extractor(x)
        
        # Predict concept probabilities
        concept_probs = self.concept_predictor(features.mean([2, 3]))
        
        # Apply attention
        attended_features = self.attention(features, concept_probs)
        
        # Route to experts
        expert_weights, expert_indices = self.router(attended_features, concept_probs)
        
        # Process with experts
        expert_outputs = []
        for i, expert in enumerate(self.experts):
            mask = (expert_indices == i).any(dim=1).float()
            expert_out = expert(attended_features) * mask.view(-1, 1, 1, 1)
            expert_outputs.append(expert_out)
        
        # Combine expert outputs
        combined = sum(expert_outputs)
        
        # Final classification
        pooled = F.adaptive_avg_pool2d(combined, 1).squeeze()
        logits = self.classifier(pooled)
        
        return {
            'logits': logits,
            'concept_probs': concept_probs,
            'expert_weights': expert_weights,
            'attention_maps': attended_features
        }
    
    def calculate_load_balancing_loss(self) -> torch.Tensor:
        """Encourage balanced expert utilization"""
        # Implementation details
        pass
    
    def calculate_concept_alignment_loss(self) -> torch.Tensor:
        """Ensure experts specialize in their assigned concepts"""
        # Implementation details
        pass

Advanced Features

A. Hierarchical Ontology Attention

class HierarchicalOntologyAttention(nn.Module):
    """Multi-level attention following ontology hierarchy"""
    
    def __init__(self, feature_dim: int, ontology_levels: List[int]):
        super().__init__()
        self.levels = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
                nn.ReLU(),
                nn.Conv2d(feature_dim, 1, 1)
            ) for _ in range(len(ontology_levels))
        ])
        
        self.output_conv = nn.Conv2d(
            feature_dim * len(ontology_levels), 
            feature_dim, 
            1
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Generate attention maps for each level
        attention_maps = [level(x) for level in self.levels]
        
        # Apply attention maps
        attended_features = []
        for attn in attention_maps:
            attn = torch.sigmoid(attn)  # [B, 1, H, W]
            attended = x * attn
            attended_features.append(attended)
        
        # Concatenate and project
        out = torch.cat(attended_features, dim=1)
        return self.output_conv(out)

Applications and Use Cases

A. Medical Image Analysis

• Pathology: Specialized experts for different tissue structures
• Radiology: Hierarchical attention for multi-scale analysis
• Ophthalmology: Concept-guided analysis of retinal images

B. Industrial Applications

• Manufacturing: Defect detection with explainable reasoning
• Autonomous Vehicles: Scene understanding with safety constraints
• Robotics: Task-specific expert modules

Performance Optimization

A. Efficient Expert Routing

class SparseExpertRouter(nn.Module):
    """Efficient routing with sparse expert selection"""
    
    def __init__(self, num_experts: int, capacity_factor: float = 1.0):
        super().__init__()
        self.num_experts = num_experts
        self.capacity_factor = capacity_factor
    
    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        # Calculate expert scores
        scores = torch.matmul(x, self.expert_weights.t())
        
        # Top-k expert selection with capacity constraints
        expert_weights, expert_indices = torch.topk(scores, k=self.top_k)
        expert_weights = F.softmax(expert_weights, dim=-1)
        
        # Apply capacity constraints
        if self.training:
            # Add noise for better exploration
            noise = torch.rand_like(expert_weights) * 0.01
            expert_weights = expert_weights + noise
            
            # Re-normalize
            expert_weights = expert_weights / expert_weights.sum(dim=-1, keepdim=True)
        
        return expert_weights, expert_indices

Evaluation and Interpretation

A. Concept Activation Analysis

def analyze_concept_activation(model: OntologyGuidedMoEVision,
                             dataloader,
                             concept_idx: int):
    """Analyze how a specific concept activates the experts"""
    model.eval()
    concept_activations = []
    
    with torch.no_grad():
        for images, _ in dataloader:
            images = images.to(device)
            outputs = model(images)
            
            # Get expert activations for this concept
            concept_mask = (outputs['concept_probs'][:, concept_idx] > 0.5).float()
            expert_weights = outputs['expert_weights'] * concept_mask.unsqueeze(-1)
            
            concept_activations.append(expert_weights.cpu().numpy())
    
    # Analyze activation patterns
    activations = np.concatenate(concept_activations, axis=0)
    mean_activations = activations.mean(axis=0)
    
    return {
        'mean_activations': mean_activations,
        'all_activations': activations
    }

Future Directions

1. Dynamic Expert Allocation: Automatically adjust the number of experts
2. Cross-Modal Integration: Combine with text and other modalities
3. Self-Supervised Learning: Learn concepts without explicit supervision
4. Federated Learning: Train across distributed datasets while preserving privacy
5. Neural-Symbolic Integration: Combine with symbolic reasoning

Implementation Tips

• Start with a small number of experts and gradually increase
• Use progressive training to stabilize learning
• Monitor expert utilization to prevent collapse
• Regularize expert specialization with appropriate loss terms
• Visualize attention maps and expert activations for debugging