defmodule Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeSynthesis do
  @moduledoc """
  Implements mechanisms for synthesizing knowledge from multiple sources.
  
  This module provides algorithms and techniques for integrating, harmonizing,
  and synthesizing knowledge from distributed sources at multiple levels of abstraction,
  enabling the emergence of collective intelligence.
  """
  
  alias Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeRepresentation
  alias KnowledgeRepresentation.KnowledgeAtom
  alias KnowledgeRepresentation.KnowledgeTriple
  alias KnowledgeRepresentation.KnowledgeFrame
  alias KnowledgeRepresentation.KnowledgeGraph
  alias KnowledgeRepresentation.HierarchicalConcept
  
  defmodule ConflictResolution do
    @moduledoc """
    Provides mechanisms for resolving conflicts in knowledge from multiple sources.
    
    This module implements various conflict resolution strategies for handling
    contradictions, inconsistencies, and divergent knowledge from different sources.
    """
    
    @doc """
    Resolves conflicts between knowledge atoms using weighted confidence.
    """
    def resolve_atom_conflict(atoms, strategy \\ :weighted_confidence) do
      case strategy do
        :weighted_confidence ->
          # Calculate weighted average of conflicting values
          total_weight = Enum.sum(Enum.map(atoms, & &1.confidence))
          
          # If all confidences are zero, use equal weights
          weights = if total_weight == 0 do
            equal_weight = 1.0 / length(atoms)
            Enum.map(atoms, fn _ -> equal_weight end)
          else
            Enum.map(atoms, fn atom -> atom.confidence / total_weight end)
          end
          
          # Calculate weighted content based on type
          sample_content = hd(atoms).content
          resolved_content = if is_number(sample_content) do
            # Numeric content - weighted average
            Enum.zip(atoms, weights)
            |> Enum.map(fn {atom, weight} -> atom.content * weight end)
            |> Enum.sum()
          else
            # Non-numeric content - highest confidence wins
            Enum.max_by(atoms, & &1.confidence).content
          end
          
          # Create new atom with resolved content
          resolved_atom = %KnowledgeAtom{
            id: "resolved_" <> hd(atoms).id,
            content: resolved_content,
            confidence: Enum.max(Enum.map(atoms, & &1.confidence)),
            source: Enum.map(atoms, & &1.source),
            timestamp: DateTime.utc_now(),
            metadata: %{
              resolution_strategy: :weighted_confidence,
              original_atoms: Enum.map(atoms, & &1.id)
            },
            context: hd(atoms).context
          }
          
          {:ok, resolved_atom}
          
        :highest_confidence ->
          # Simply take the atom with highest confidence
          resolved_atom = Enum.max_by(atoms, & &1.confidence)
          
          {:ok, %{resolved_atom | 
            metadata: Map.put(resolved_atom.metadata, :resolution_strategy, :highest_confidence)
          }}
          
        :newest ->
          # Take the most recent atom
          resolved_atom = Enum.max_by(atoms, fn atom ->
            DateTime.to_unix(atom.timestamp)
          end)
          
          {:ok, %{resolved_atom | 
            metadata: Map.put(resolved_atom.metadata, :resolution_strategy, :newest)
          }}
          
        :voting ->
          # Group by content and count occurrences
          content_counts = Enum.reduce(atoms, %{}, fn atom, acc ->
            Map.update(acc, atom.content, 1, &(&1 + 1))
          end)
          
          # Find content with most votes
          {winning_content, _count} = Enum.max_by(content_counts, fn {_content, count} -> count end)
          
          # Use the highest confidence atom with winning content
          winning_atoms = Enum.filter(atoms, fn atom -> atom.content == winning_content end)
          resolved_atom = Enum.max_by(winning_atoms, & &1.confidence)
          
          {:ok, %{resolved_atom | 
            metadata: Map.put(resolved_atom.metadata, :resolution_strategy, :voting)
          }}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom resolution function
          case strategy.(atoms) do
            {:ok, resolved} -> {:ok, resolved}
            _ -> {:error, :custom_resolution_failed}
          end
          
        _ ->
          {:error, :unknown_resolution_strategy}
      end
    end
    
    @doc """
    Resolves conflicts between knowledge triples.
    """
    def resolve_triple_conflict(triples, strategy \\ :weighted_confidence) do
      case strategy do
        :weighted_confidence ->
          # Group triples by predicate
          triples_by_predicate = Enum.group_by(triples, & &1.predicate)
          
          # Resolve each predicate group separately
          resolved_triples = Enum.map(triples_by_predicate, fn {predicate, pred_triples} ->
            # Further group by object type
            triples_by_object_type = Enum.group_by(pred_triples, fn triple ->
              cond do
                is_number(triple.object) -> :numeric
                is_binary(triple.object) -> :string
                true -> :other
              end
            end)
            
            # Resolve each object type group
            Enum.flat_map(triples_by_object_type, fn {_type, type_triples} ->
              # For numeric objects, use weighted average
              if _type == :numeric do
                total_weight = Enum.sum(Enum.map(type_triples, & &1.confidence))
                weighted_object = Enum.reduce(type_triples, 0, fn triple, acc ->
                  acc + (triple.object * triple.confidence / total_weight)
                end)
                
                # Create resolved triple
                resolved_triple = %KnowledgeTriple{
                  id: "resolved_" <> hd(type_triples).id,
                  subject: hd(type_triples).subject,
                  predicate: predicate,
                  object: weighted_object,
                  confidence: Enum.max(Enum.map(type_triples, & &1.confidence)),
                  source: Enum.map(type_triples, & &1.source),
                  timestamp: DateTime.utc_now(),
                  metadata: %{
                    resolution_strategy: :weighted_confidence,
                    original_triples: Enum.map(type_triples, & &1.id)
                  },
                  context: hd(type_triples).context
                }
                
                [resolved_triple]
              else
                # For non-numeric objects, use highest confidence
                [Enum.max_by(type_triples, & &1.confidence)]
              end
            end)
          end)
          |> List.flatten()
          
          {:ok, resolved_triples}
          
        :highest_confidence ->
          # Group triples by predicate
          triples_by_predicate = Enum.group_by(triples, & &1.predicate)
          
          # For each predicate, take triple with highest confidence
          resolved_triples = Enum.map(triples_by_predicate, fn {_predicate, pred_triples} ->
            Enum.max_by(pred_triples, & &1.confidence)
          end)
          
          {:ok, resolved_triples}
          
        :all ->
          # Keep all triples - no resolution needed
          {:ok, triples}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom resolution function
          case strategy.(triples) do
            {:ok, resolved} -> {:ok, resolved}
            _ -> {:error, :custom_resolution_failed}
          end
          
        _ ->
          {:error, :unknown_resolution_strategy}
      end
    end
    
    @doc """
    Resolves conflicts between knowledge frames.
    """
    def resolve_frame_conflict(frames, strategy \\ :slot_by_slot) do
      case strategy do
        :slot_by_slot ->
          # Get all slot names across all frames
          all_slots = Enum.reduce(frames, MapSet.new(), fn frame, acc ->
            MapSet.union(acc, MapSet.new(Map.keys(frame.slots)))
          end)
          
          # For each slot, take the value with highest confidence
          resolved_slots = Enum.reduce(all_slots, %{}, fn slot_name, acc ->
            # Get all values for this slot across frames
            slot_values = Enum.map(frames, fn frame ->
              Map.get(frame.slots, slot_name)
            end)
            |> Enum.reject(&is_nil/1)
            
            if Enum.empty?(slot_values) do
              acc
            else
              # Take slot with highest confidence
              best_slot = Enum.max_by(slot_values, & &1.confidence)
              Map.put(acc, slot_name, best_slot)
            end
          end)
          
          # Create merged frame
          merged_frame = %KnowledgeFrame{
            id: "resolved_" <> hd(frames).id,
            name: "Resolved_" <> hd(frames).name,
            slots: resolved_slots,
            parent: nil,  # No parent for merged frame
            confidence: Enum.max(Enum.map(frames, & &1.confidence)),
            source: Enum.map(frames, & &1.source),
            timestamp: DateTime.utc_now(),
            metadata: %{
              resolution_strategy: :slot_by_slot,
              original_frames: Enum.map(frames, & &1.id)
            }
          }
          
          {:ok, merged_frame}
          
        :highest_confidence_frame ->
          # Simply take the frame with highest overall confidence
          resolved_frame = Enum.max_by(frames, & &1.confidence)
          
          {:ok, %{resolved_frame | 
            metadata: Map.put(resolved_frame.metadata, :resolution_strategy, :highest_confidence_frame)
          }}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom resolution function
          case strategy.(frames) do
            {:ok, resolved} -> {:ok, resolved}
            _ -> {:error, :custom_resolution_failed}
          end
          
        _ ->
          {:error, :unknown_resolution_strategy}
      end
    end
  end
  
  defmodule AbstractionSynthesis do
    @moduledoc """
    Provides mechanisms for synthesizing abstract knowledge from concrete instances.
    
    This module implements techniques for generalizing from specific knowledge
    instances to create higher-level abstractions, patterns, and concepts.
    """
    
    @doc """
    Creates an abstract concept from a collection of concrete instances.
    """
    def abstract_concept_from_instances(instances, name, description, opts \\ []) do
      # Extract common attributes
      common_attributes = extract_common_attributes(instances)
      
      # Create hierarchical concept
      concept = HierarchicalConcept.new(
        name,
        description,
        Keyword.put(opts, :attributes, common_attributes)
      )
      
      # Create parent-child relationships
      {updated_concept, updated_instances} = Enum.reduce(instances, {concept, []}, fn instance, {concept_acc, instances_acc} ->
        {updated_concept, updated_instance} = HierarchicalConcept.add_child(concept_acc, instance)
        {updated_concept, [updated_instance | instances_acc]}
      end)
      
      {:ok, updated_concept, updated_instances}
    end
    
    @doc """
    Creates a frame template from a collection of frame instances.
    """
    def frame_template_from_instances(frames, name, opts \\ []) do
      # Extract common slots
      common_slots = extract_common_slots(frames)
      
      # Create frame template
      template = KnowledgeFrame.new(
        name,
        common_slots,
        opts
      )
      
      {:ok, template}
    end
    
    @doc """
    Synthesizes a knowledge graph pattern from recurring subgraphs.
    """
    def graph_pattern_from_instances(graphs, pattern_name, min_support \\ 0.5) do
      # Extract frequent subgraphs
      frequent_patterns = extract_frequent_subgraphs(graphs, min_support)
      
      if Enum.empty?(frequent_patterns) do
        {:error, :no_patterns_found}
      else
        # Create a new knowledge graph with the most common pattern
        {entities, relationships} = hd(frequent_patterns)
        
        pattern_graph = %KnowledgeGraph{
          id: "pattern_" <> Base.encode16(:crypto.strong_rand_bytes(6), case: :lower),
          name: pattern_name,
          entities: entities,
          relationships: relationships,
          metadata: %{
            pattern_type: :frequent_subgraph,
            support: length(frequent_patterns) / length(graphs)
          },
          timestamp: DateTime.utc_now()
        }
        
        {:ok, pattern_graph}
      end
    end
    
    # Private functions
    
    defp extract_common_attributes(instances) do
      # Initialize with first instance's attributes
      first_attrs = hd(instances).attributes
      
      # Find attributes common to all instances
      Enum.reduce(tl(instances), first_attrs, fn instance, common ->
        # Keep only attributes that exist in both
        Map.filter(common, fn {key, _} ->
          Map.has_key?(instance.attributes, key)
        end)
      end)
    end
    
    defp extract_common_slots(frames) do
      # Initialize with first frame's slots
      first_slots = hd(frames).slots
      
      # Find slots common to all frames
      common_slot_names = Enum.reduce(tl(frames), Map.keys(first_slots), fn frame, common ->
        # Keep only slot names that exist in both
        Enum.filter(common, fn name -> Map.has_key?(frame.slots, name) end)
      end)
      
      # Extract slots from first frame (could average/merge values from all frames)
      Enum.map(common_slot_names, fn name ->
        {name, Map.get(first_slots, name)}
      end)
      |> Enum.into(%{})
    end
    
    defp extract_frequent_subgraphs(_graphs, _min_support) do
      # This is a complex algorithm that would typically implement
      # a variant of the gSpan algorithm for frequent subgraph mining
      
      # For the sake of this implementation, we'll return a placeholder
      # In a real implementation, this would analyze the graphs to find common patterns
      [
        {%{"entity1" => %{id: "entity1", type: :abstract, properties: %{}, confidence: 1.0},
           "entity2" => %{id: "entity2", type: :abstract, properties: %{}, confidence: 1.0}},
         %{"rel1" => %{id: "rel1", type: :abstract_relation, from: "entity1", to: "entity2", properties: %{}, confidence: 1.0}}}
      ]
    end
  end
  
  defmodule IntegrationSynthesis do
    @moduledoc """
    Provides mechanisms for horizontally integrating knowledge from different sources.
    
    This module implements techniques for combining, merging, and integrating knowledge
    across different domains, perspectives, or sources while maintaining coherence.
    """
    
    @doc """
    Integrates multiple knowledge atoms into a coherent set.
    """
    def integrate_atoms(atoms, strategy \\ :cluster_and_resolve) do
      case strategy do
        :cluster_and_resolve ->
          # Group atoms by content similarity
          clusters = cluster_by_similarity(atoms, &content_similarity/2, 0.7)
          
          # Resolve conflicts within each cluster
          resolved_atoms = Enum.map(clusters, fn cluster ->
            case ConflictResolution.resolve_atom_conflict(cluster) do
              {:ok, resolved} -> resolved
              _ -> hd(cluster)  # Fallback to first atom in cluster
            end
          end)
          
          {:ok, resolved_atoms}
          
        :all ->
          # Keep all atoms without integration
          {:ok, atoms}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom integration function
          case strategy.(atoms) do
            {:ok, integrated} -> {:ok, integrated}
            _ -> {:error, :custom_integration_failed}
          end
          
        _ ->
          {:error, :unknown_integration_strategy}
      end
    end
    
    @doc """
    Integrates multiple knowledge triples into a coherent graph.
    """
    def integrate_triples(triples, strategy \\ :graph_merge) do
      case strategy do
        :graph_merge ->
          # Convert triples to graph structure
          graph = triples_to_graph(triples)
          
          {:ok, graph}
          
        :cluster_predicates ->
          # Group triples by predicate
          by_predicate = Enum.group_by(triples, & &1.predicate)
          
          # Resolve conflicts within each predicate group
          resolved_groups = Enum.map(by_predicate, fn {_predicate, pred_triples} ->
            case ConflictResolution.resolve_triple_conflict(pred_triples) do
              {:ok, resolved} -> resolved
              _ -> pred_triples  # Fallback to original triples
            end
          end)
          |> List.flatten()
          
          {:ok, resolved_groups}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom integration function
          case strategy.(triples) do
            {:ok, integrated} -> {:ok, integrated}
            _ -> {:error, :custom_integration_failed}
          end
          
        _ ->
          {:error, :unknown_integration_strategy}
      end
    end
    
    @doc """
    Integrates multiple knowledge frames into a unified structure.
    """
    def integrate_frames(frames, strategy \\ :hierarchical_merge) do
      case strategy do
        :hierarchical_merge ->
          # Group frames by parent
          by_parent = Enum.group_by(frames, & &1.parent)
          
          # Process frames with no parent first
          root_frames = Map.get(by_parent, nil, [])
          
          if Enum.empty?(root_frames) do
            # No root frames, just merge all
            case ConflictResolution.resolve_frame_conflict(frames) do
              {:ok, merged} -> {:ok, merged}
              _ -> {:error, :frame_merge_failed}
            end
          else
            # Merge root frames
            {:ok, merged_root} = ConflictResolution.resolve_frame_conflict(root_frames)
            
            # Process child frames
            processed_frames = [merged_root]
            
            # TODO: Process hierarchical structure fully
            # This would involve recursively processing children
            
            {:ok, processed_frames}
          end
          
        :slot_merge ->
          # Merge all frames slot by slot
          case ConflictResolution.resolve_frame_conflict(frames, :slot_by_slot) do
            {:ok, merged} -> {:ok, merged}
            _ -> {:error, :frame_merge_failed}
          end
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom integration function
          case strategy.(frames) do
            {:ok, integrated} -> {:ok, integrated}
            _ -> {:error, :custom_integration_failed}
          end
          
        _ ->
          {:error, :unknown_integration_strategy}
      end
    end
    
    @doc """
    Integrates multiple knowledge graphs into a unified graph.
    """
    def integrate_graphs(graphs, strategy \\ :union_merge) do
      case strategy do
        :union_merge ->
          # Create union of all graphs
          merged_graph = Enum.reduce(tl(graphs), hd(graphs), fn graph, acc ->
            KnowledgeGraph.merge(acc, graph)
          end)
          
          {:ok, merged_graph}
          
        :intersection_merge ->
          # Find entities and relationships common to all graphs
          first = hd(graphs)
          rest = tl(graphs)
          
          # Find common entities
          common_entities = Enum.reduce(rest, first.entities, fn graph, common ->
            # Keep only entities that exist in both
            Map.filter(common, fn {key, _} ->
              Map.has_key?(graph.entities, key)
            end)
          end)
          
          # Find common relationships
          common_relationships = Enum.reduce(rest, first.relationships, fn graph, common ->
            # Keep only relationships that exist in both
            Map.filter(common, fn {key, _} ->
              Map.has_key?(graph.relationships, key)
            end)
          end)
          
          # Create intersection graph
          intersection_graph = %KnowledgeGraph{
            id: "intersection_" <> Base.encode16(:crypto.strong_rand_bytes(6), case: :lower),
            name: "Intersection of #{length(graphs)} graphs",
            entities: common_entities,
            relationships: common_relationships,
            metadata: %{integration_type: :intersection},
            timestamp: DateTime.utc_now()
          }
          
          {:ok, intersection_graph}
          
        :custom_strategy when is_function(strategy, 1) ->
          # Use custom integration function
          case strategy.(graphs) do
            {:ok, integrated} -> {:ok, integrated}
            _ -> {:error, :custom_integration_failed}
          end
          
        _ ->
          {:error, :unknown_integration_strategy}
      end
    end
    
    # Private functions
    
    defp cluster_by_similarity(items, similarity_fn, threshold) do
      # Start with each item in its own cluster
      initial_clusters = Enum.map(items, fn item -> [item] end)
      
      # Iteratively merge clusters
      cluster_until_stable(initial_clusters, similarity_fn, threshold)
    end
    
    defp cluster_until_stable(clusters, similarity_fn, threshold) do
      # Try to merge any pair of clusters
      {new_clusters, merged} = merge_similar_clusters(clusters, similarity_fn, threshold)
      
      # If no merges made, we're done
      if merged do
        # Continue merging
        cluster_until_stable(new_clusters, similarity_fn, threshold)
      else
        # Stable clustering achieved
        new_clusters
      end
    end
    
    defp merge_similar_clusters(clusters, similarity_fn, threshold) do
      # Check all pairs of clusters
      cluster_pairs = for i <- 0..(length(clusters) - 1),
                          j <- (i + 1)..(length(clusters) - 1),
                          do: {Enum.at(clusters, i), Enum.at(clusters, j)}
      
      # Try to find a pair to merge
      case Enum.find(cluster_pairs, fn {cluster1, cluster2} ->
        # Calculate average similarity between all pairs of items
        pairs = for item1 <- cluster1, item2 <- cluster2, do: {item1, item2}
        
        if Enum.empty?(pairs) do
          false
        else
          total_similarity = Enum.reduce(pairs, 0, fn {item1, item2}, acc ->
            acc + similarity_fn.(item1, item2)
          end)
          
          avg_similarity = total_similarity / length(pairs)
          avg_similarity >= threshold
        end
      end) do
        nil ->
          # No pair found, clustering is stable
          {clusters, false}
          
        {cluster1, cluster2} ->
          # Merge the clusters
          merged_cluster = cluster1 ++ cluster2
          remaining_clusters = clusters -- [cluster1, cluster2]
          
          {[merged_cluster | remaining_clusters], true}
      end
    end
    
    defp content_similarity(atom1, atom2) do
      cond do
        # Same content
        atom1.content == atom2.content ->
          1.0
          
        # Both numeric
        is_number(atom1.content) and is_number(atom2.content) ->
          # Simple numeric similarity
          max_val = max(abs(atom1.content), abs(atom2.content))
          
          if max_val == 0 do
            1.0  # Both zero
          else
            1.0 - min(1.0, abs(atom1.content - atom2.content) / max_val)
          end
          
        # Both strings
        is_binary(atom1.content) and is_binary(atom2.content) ->
          # Simple string similarity (Levenshtein-based)
          string_similarity(atom1.content, atom2.content)
          
        # Different types
        true ->
          0.0
      end
    end
    
    defp string_similarity(str1, str2) do
      # Simple string similarity based on character overlap
      # In a real implementation, use a proper string distance function
      
      chars1 = String.graphemes(str1) |> MapSet.new()
      chars2 = String.graphemes(str2) |> MapSet.new()
      
      intersection = MapSet.intersection(chars1, chars2) |> MapSet.size()
      union = MapSet.union(chars1, chars2) |> MapSet.size()
      
      if union == 0, do: 1.0, else: intersection / union
    end
    
    defp triples_to_graph(triples) do
      # Extract entities from subjects and objects
      subject_entities = Enum.map(triples, fn triple ->
        {triple.subject, %{
          id: to_string(triple.subject),
          type: :subject,
          properties: %{},
          confidence: triple.confidence
        }}
      end)
      |> Enum.into(%{})
      
      object_entities = Enum.map(triples, fn triple ->
        {triple.object, %{
          id: to_string(triple.object),
          type: :object,
          properties: %{},
          confidence: triple.confidence
        }}
      end)
      |> Enum.into(%{})
      
      # Merge subject and object entities
      entities = Map.merge(subject_entities, object_entities)
      
      # Convert triples to relationships
      relationships = Enum.map(triples, fn triple ->
        {triple.id, %{
          id: triple.id,
          type: triple.predicate,
          from: to_string(triple.subject),
          to: to_string(triple.object),
          properties: %{},
          confidence: triple.confidence
        }}
      end)
      |> Enum.into(%{})
      
      # Create graph
      %KnowledgeGraph{
        id: "graph_from_triples_" <> Base.encode16(:crypto.strong_rand_bytes(6), case: :lower),
        name: "Graph from #{length(triples)} triples",
        entities: entities,
        relationships: relationships,
        metadata: %{source: :triples_conversion},
        timestamp: DateTime.utc_now()
      }
    end
  end
  
  defmodule ConsistencyVerification do
    @moduledoc """
    Provides mechanisms for verifying the consistency of synthesized knowledge.
    
    This module implements techniques for detecting and resolving logical
    inconsistencies, contradictions, and incoherence in synthesized knowledge.
    """
    
    @doc """
    Verifies the logical consistency of a set of knowledge atoms.
    """
    def verify_atom_consistency(atoms) do
      # Group atoms by content type
      typed_atoms = group_atoms_by_type(atoms)
      
      # Check consistency for each type
      results = Enum.map(typed_atoms, fn {type, atoms} ->
        case type do
          :numeric -> verify_numeric_consistency(atoms)
          :boolean -> verify_boolean_consistency(atoms)
          :string -> {:ok, atoms} # Strings can have different values
          _ -> {:ok, atoms} # Other types are treated as consistent by default
        end
      end)
      
      # Check if any inconsistencies were found
      if Enum.any?(results, fn result -> elem(result, 0) == :inconsistent end) do
        # Get inconsistencies
        inconsistencies = Enum.filter(results, fn result -> elem(result, 0) == :inconsistent end)
        |> Enum.map(fn {:inconsistent, atoms, reason} -> {atoms, reason} end)
        
        {:inconsistent, inconsistencies}
      else
        # All consistent
        {:ok, atoms}
      end
    end
    
    @doc """
    Verifies the logical consistency of a set of knowledge triples.
    """
    def verify_triple_consistency(triples) do
      # Check for direct contradictions
      contradictions = find_triple_contradictions(triples)
      
      if Enum.empty?(contradictions) do
        # No direct contradictions
        {:ok, triples}
      else
        # Found contradictions
        {:inconsistent, contradictions}
      end
    end
    
    @doc """
    Verifies the consistency of a knowledge graph.
    """
    def verify_graph_consistency(graph) do
      # Convert relationships to triples for contradiction detection
      triples = relationships_to_triples(graph.relationships)
      
      # Check for contradictions
      contradictions = find_triple_contradictions(triples)
      
      if Enum.empty?(contradictions) do
        # Check additional graph-specific constraints
        issues = check_graph_constraints(graph)
        
        if Enum.empty?(issues) do
          # Graph is consistent
          {:ok, graph}
        else
          # Graph has issues
          {:inconsistent, issues}
        end
      else
        # Found contradictions in relationships
        {:inconsistent, contradictions}
      end
    end
    
    # Private functions
    
    defp group_atoms_by_type(atoms) do
      Enum.group_by(atoms, fn atom ->
        cond do
          is_number(atom.content) -> :numeric
          is_boolean(atom.content) -> :boolean
          is_binary(atom.content) -> :string
          true -> :other
        end
      end)
    end
    
    defp verify_numeric_consistency(atoms) do
      # Group atoms by source
      by_source = Enum.group_by(atoms, & &1.source)
      
      # Check for large variance between sources
      all_values = Enum.map(atoms, & &1.content)
      variance = calculate_variance(all_values)
      
      if variance > 0.5 * Enum.max(all_values) do
        # High variance indicates inconsistency
        {:inconsistent, atoms, :high_variance}
      else
        # Variance is acceptable
        {:ok, atoms}
      end
    end
    
    defp verify_boolean_consistency(atoms) do
      # Check if there are conflicting boolean values
      values = Enum.map(atoms, & &1.content)
      
      if Enum.member?(values, true) and Enum.member?(values, false) do
        # Both true and false present - inconsistent
        {:inconsistent, atoms, :boolean_contradiction}
      else
        # All same value - consistent
        {:ok, atoms}
      end
    end
    
    defp calculate_variance(values) do
      n = length(values)
      
      if n <= 1 do
        0.0
      else
        mean = Enum.sum(values) / n
        
        sum_squared_diff = Enum.reduce(values, 0, fn val, acc ->
          diff = val - mean
          acc + diff * diff
        end)
        
        sum_squared_diff / (n - 1)
      end
    end
    
    defp find_triple_contradictions(triples) do
      # Group by subject and predicate
      by_subject_predicate = Enum.group_by(triples, fn triple ->
        {triple.subject, triple.predicate}
      end)
      
      # Find groups with conflicting objects
      Enum.filter(by_subject_predicate, fn {{_subject, predicate}, group} ->
        # Check if this predicate should have unique values
        if is_functional_predicate?(predicate) do
          # Check if there are different objects
          objects = Enum.map(group, & &1.object)
          length(Enum.uniq(objects)) > 1
        else
          false
        end
      end)
      |> Enum.map(fn {_key, group} -> group end)
    end
    
    defp is_functional_predicate?(predicate) do
      # Predicates that should have unique values
      functional_predicates = [
        :equals, :age, :height, :weight, :value, :identifier, :id,
        :date_of_birth, :created_at, :timestamp
      ]
      
      predicate in functional_predicates or 
        (is_binary(predicate) and String.contains?(to_string(predicate), ["equals", "age", "height", "weight", "value", "id"]))
    end
    
    defp relationships_to_triples(relationships) do
      Enum.map(relationships, fn {_id, rel} ->
        %KnowledgeTriple{
          id: rel.id,
          subject: rel.from,
          predicate: rel.type,
          object: rel.to,
          confidence: rel.confidence,
          source: :graph_relationship,
          timestamp: DateTime.utc_now(),
          metadata: %{},
          context: %{}
        }
      end)
    end
    
    defp check_graph_constraints(graph) do
      # Check for orphaned entities (no connections)
      orphaned = find_orphaned_entities(graph)
      
      # Check for cycles in hierarchical relationships
      hierarchical_cycles = find_hierarchical_cycles(graph)
      
      # Collect all issues
      orphaned_issues = if Enum.empty?(orphaned), do: [], else: [{:orphaned_entities, orphaned}]
      cycle_issues = if Enum.empty?(hierarchical_cycles), do: [], else: [{:hierarchical_cycles, hierarchical_cycles}]
      
      orphaned_issues ++ cycle_issues
    end
    
    defp find_orphaned_entities(graph) do
      # Find entities with no incoming or outgoing relationships
      all_entity_ids = Map.keys(graph.entities)
      
      connected_entity_ids = Enum.reduce(graph.relationships, MapSet.new(), fn {_id, rel}, acc ->
        acc
        |> MapSet.put(rel.from)
        |> MapSet.put(rel.to)
      end)
      
      # Find entities that are not connected
      Enum.filter(all_entity_ids, fn id ->
        not MapSet.member?(connected_entity_ids, id)
      end)
    end
    
    defp find_hierarchical_cycles(graph) do
      # Find cycles in hierarchical relationships like "parent_of", "contains", etc.
      hierarchical_rels = Enum.filter(graph.relationships, fn {_id, rel} ->
        is_hierarchical_relation?(rel.type)
      end)
      |> Enum.map(fn {_id, rel} -> rel end)
      
      # Build adjacency list for hierarchical relationships
      adjacency = Enum.reduce(hierarchical_rels, %{}, fn rel, acc ->
        # Add edge from parent to child
        Map.update(acc, rel.from, [rel.to], fn existing -> [rel.to | existing] end)
      end)
      
      # Find cycles using DFS
      cycles = []
      visited = MapSet.new()
      path = MapSet.new()
      
      # Check each node for cycles
      Enum.reduce(Map.keys(adjacency), cycles, fn node, acc ->
        if MapSet.member?(visited, node) do
          acc
        else
          {new_visited, new_path, cycles} = detect_cycles(node, adjacency, visited, path, [])
          
          # Add found cycles to accumulated cycles
          acc ++ cycles
        end
      end)
    end
    
    defp is_hierarchical_relation?(type) do
      hierarchical_types = [
        :parent_of, :contains, :includes, :has_part, :broader_than,
        :superclass_of, :subsumes
      ]
      
      type in hierarchical_types or 
        (is_binary(type) and String.contains?(to_string(type), ["parent", "contain", "include", "has", "broader", "super", "sub"]))
    end
    
    defp detect_cycles(node, adjacency, visited, path, cycles) do
      # Mark node as visited and add to current path
      visited = MapSet.put(visited, node)
      path = MapSet.put(path, node)
      
      # Get neighbors
      neighbors = Map.get(adjacency, node, [])
      
      # Check each neighbor
      {new_visited, new_path, new_cycles} = Enum.reduce(neighbors, {visited, path, cycles}, fn neighbor, {v, p, c} ->
        if MapSet.member?(p, neighbor) do
          # Found a cycle
          cycle = extract_cycle(neighbor, node, adjacency)
          {v, p, [cycle | c]}
        else
          if MapSet.member?(v, neighbor) do
            # Already visited, no cycle through this node
            {v, p, c}
          else
            # Continue DFS
            detect_cycles(neighbor, adjacency, v, p, c)
          end
        end
      end)
      
      # Remove node from current path
      new_path = MapSet.delete(new_path, node)
      
      {new_visited, new_path, new_cycles}
    end
    
    defp extract_cycle(start, current, adjacency) do
      # Extract the cycle starting from 'start' and going through 'current'
      
      # This is a simplified implementation that may not extract the exact cycle
      # In a real implementation, you would track the path during DFS
      [start, current]
    end
  end
end