# Advanced Knowledge Extraction Algorithms
**Version:** 2.23.16
**Status:** Design Proposal
**Last Updated:** 2026-01-03
Proposal for extracting deeper insights from the TDZ C64 Knowledge Base using advanced algorithms and making this information accessible through new MCP tools and visualizations.
---
## 📋 Current Capabilities
**Existing Knowledge Extraction:**
- ✅ Entity extraction (50+ entity types, regex + LLM)
- ✅ Entity relationships (distance-based co-occurrence)
- ✅ Semantic embeddings (384-dimensional vectors, FAISS index)
- ✅ Document comparison (metadata, content, full)
- ✅ Faceted search (entity-based filtering)
- ✅ RAG question answering
**Current Statistics:**
- 199 documents
- 5,044 chunks
- 7.4M words
- Semantic embeddings built
- Entity extraction available
---
## 🎯 Proposed Knowledge Extraction Algorithms
### 1. Knowledge Graph Analysis ⭐⭐⭐⭐⭐
**What:** Build and analyze a graph database of entities and their relationships
**Algorithms:**
- **PageRank** - Find most important/central entities
- **Community Detection** - Discover entity clusters (Louvain algorithm)
- **Shortest Path** - Find connections between concepts
- **Centrality Measures** - Identify hub entities (betweenness, closeness)
- **Graph Clustering** - Group related entities
**Example Insights:**
```
Most Central Entities (PageRank):
1. VIC-II (score: 0.234) - Connected to 127 other entities
2. SID chip (score: 0.198) - Connected to 94 other entities
3. 6502 CPU (score: 0.176) - Connected to 82 other entities
Communities Detected:
- Audio/Music: SID, waveforms, filters, ADSR
- Graphics: VIC-II, sprites, rasters, colors
- Programming: 6502, assembly, BASIC, memory
```
**Implementation:**
```python
def build_knowledge_graph(self) -> nx.Graph:
"""Build NetworkX graph from entities and relationships."""
G = nx.Graph()
# Add nodes (entities)
entities = self._get_all_entities()
for entity in entities:
G.add_node(entity['text'],
type=entity['type'],
occurrences=entity['count'])
# Add edges (relationships)
relationships = self._get_all_relationships()
for rel in relationships:
G.add_edge(rel['entity1'], rel['entity2'],
weight=rel['strength'],
co_occurrences=rel['count'])
return G
def analyze_knowledge_graph(self, G: nx.Graph) -> dict:
"""Run graph algorithms to extract insights."""
return {
'pagerank': nx.pagerank(G, weight='weight'),
'communities': nx.community.louvain_communities(G),
'centrality': nx.betweenness_centrality(G),
'clustering': nx.clustering(G),
'diameter': nx.diameter(G) if nx.is_connected(G) else None
}
def find_entity_path(self, entity1: str, entity2: str) -> list:
"""Find shortest path between two entities."""
G = self.build_knowledge_graph()
try:
path = nx.shortest_path(G, entity1, entity2)
return {
'path': path,
'length': len(path) - 1,
'explanation': self._explain_path(path)
}
except nx.NetworkXNoPath:
return {'path': None, 'message': 'No connection found'}
```
**MCP Tools:**
- `analyze_knowledge_graph` - Get graph statistics
- `find_entity_path` - Find connections between concepts
- `get_central_entities` - List most important entities
- `discover_communities` - Find entity clusters
---
### 2. Topic Modeling ⭐⭐⭐⭐
**What:** Discover hidden topics in the corpus using unsupervised learning
**Algorithms:**
- **LDA (Latent Dirichlet Allocation)** - Probabilistic topic modeling
- **NMF (Non-negative Matrix Factorization)** - Linear algebra approach
- **BERTopic** - Modern topic modeling with transformers
**Example Insights:**
```
Topic 0: Graphics Programming (18% of corpus)
Top words: sprite, VIC-II, raster, screen, color, bitmap
Documents: 34 documents strongly associated
Topic 1: Audio/Music (15% of corpus)
Top words: SID, sound, waveform, frequency, filter, ADSR
Documents: 28 documents strongly associated
Topic 2: Hardware Architecture (12% of corpus)
Top words: memory, address, register, chip, CIA, RAM
Documents: 22 documents strongly associated
```
**Implementation:**
```python
from sklearn.decomposition import LatentDirichletAllocation, NMF
from sklearn.feature_extraction.text import TfidfVectorizer
def discover_topics(self, num_topics: int = 10, method: str = 'lda') -> dict:
"""
Discover topics in the corpus.
Args:
num_topics: Number of topics to extract
method: 'lda', 'nmf', or 'bertopic'
Returns:
{
'topics': [{'id': 0, 'top_words': [...], 'weight': 0.18}],
'document_topics': {doc_id: [topic_weights]},
'topic_evolution': {...} # How topics change over time
}
"""
# Get all document texts
texts = [self._get_document_text(doc_id)
for doc_id in self.documents.keys()]
# Vectorize
vectorizer = TfidfVectorizer(max_features=1000,
stop_words='english')
doc_term_matrix = vectorizer.fit_transform(texts)
# Apply topic model
if method == 'lda':
model = LatentDirichletAllocation(n_components=num_topics,
random_state=42)
elif method == 'nmf':
model = NMF(n_components=num_topics, random_state=42)
doc_topic_matrix = model.fit_transform(doc_term_matrix)
# Extract topics
topics = []
feature_names = vectorizer.get_feature_names_out()
for topic_idx, topic in enumerate(model.components_):
top_indices = topic.argsort()[-10:][::-1]
top_words = [feature_names[i] for i in top_indices]
topics.append({
'id': topic_idx,
'top_words': top_words,
'weight': doc_topic_matrix[:, topic_idx].mean()
})
return {
'topics': topics,
'document_topics': self._assign_topics(doc_topic_matrix),
'model': model
}
def get_topic_timeline(self, topic_id: int) -> dict:
"""Track how a topic evolves over time."""
# Analyze documents by date, track topic prevalence
pass
```
**MCP Tools:**
- `discover_topics` - Extract topics from corpus
- `get_document_topics` - Show topics for a document
- `search_by_topic` - Find documents in a topic
- `get_topic_timeline` - Track topic evolution
---
### 3. Document Clustering ⭐⭐⭐⭐
**What:** Group similar documents into clusters
**Algorithms:**
- **K-Means** - Partition documents into K clusters
- **Hierarchical Clustering** - Build dendrogram of document relationships
- **DBSCAN** - Density-based clustering (finds natural clusters)
- **HDBSCAN** - Hierarchical DBSCAN (better for varying densities)
**Example Insights:**
```
Cluster 0: VIC-II Programming Guides (23 documents)
- Average similarity: 0.87
- Key topics: sprites, rasters, interrupts
- Recommended for: Graphics programming
Cluster 1: SID Music Tutorials (18 documents)
- Average similarity: 0.91
- Key topics: waveforms, ADSR, filters
- Recommended for: Music composition
Cluster 2: Memory Maps & Hardware Reference (31 documents)
- Average similarity: 0.76
- Key topics: addresses, registers, I/O
- Recommended for: Hardware understanding
```
**Implementation:**
```python
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.metrics import silhouette_score
def cluster_documents(self, n_clusters: int = 10,
method: str = 'kmeans') -> dict:
"""
Cluster documents by similarity.
Args:
n_clusters: Number of clusters (for kmeans, hierarchical)
method: 'kmeans', 'hierarchical', 'dbscan', 'hdbscan'
Returns:
{
'clusters': [{
'id': 0,
'documents': [doc_ids],
'centroid_topics': [...],
'avg_similarity': 0.87,
'size': 23
}],
'quality_score': 0.65 # Silhouette score
}
"""
# Get embeddings for all documents
embeddings = self._get_document_embeddings()
# Apply clustering
if method == 'kmeans':
clusterer = KMeans(n_clusters=n_clusters, random_state=42)
elif method == 'hierarchical':
clusterer = AgglomerativeClustering(n_clusters=n_clusters)
elif method == 'dbscan':
clusterer = DBSCAN(eps=0.3, min_samples=2)
cluster_labels = clusterer.fit_predict(embeddings)
# Build cluster info
clusters = self._build_cluster_info(cluster_labels, embeddings)
# Calculate quality
quality = silhouette_score(embeddings, cluster_labels)
return {
'clusters': clusters,
'quality_score': quality,
'method': method
}
def get_cluster_summary(self, cluster_id: int) -> dict:
"""Get detailed summary of a cluster."""
return {
'documents': [...],
'common_entities': [...], # Most frequent entities
'common_topics': [...], # Topic distribution
'representative_doc': doc_id, # Document closest to centroid
'outliers': [...] # Documents on cluster edge
}
```
**MCP Tools:**
- `cluster_documents` - Group similar documents
- `get_cluster_summary` - Detailed cluster analysis
- `find_document_cluster` - Which cluster is a document in?
- `recommend_similar_clusters` - Find related clusters
---
### 4. Timeline & Event Extraction ⭐⭐⭐⭐
**What:** Extract temporal information and build chronological timelines
**Algorithms:**
- **Date/Time Extraction** - Regex + NER for dates
- **Event Detection** - Identify significant events
- **Temporal Ordering** - Order events chronologically
- **Trend Analysis** - Track how topics/entities change over time
**Example Insights:**
```
C64 Historical Timeline:
1982:
- VIC-II chip released (MOS Technology)
- C64 announced at CES
- First units shipped
1983:
- SID chip documented (Bob Yannes)
- BASIC 2.0 released
- 1 million units sold
1985:
- C64C revision released
- SID 8580 variant introduced
Technology Evolution:
- Early focus: Hardware specs, memory maps
- Mid-period: Programming techniques, demos
- Later: Preservation, emulation, modern tools
```
**Implementation:**
```python
import re
from datetime import datetime
def extract_timeline(self) -> dict:
"""
Extract chronological timeline from documents.
Returns:
{
'events': [{
'date': '1982-08-01',
'event': 'C64 released',
'entities': ['Commodore', 'VIC-II', 'SID'],
'source_docs': [doc_ids],
'confidence': 0.95
}],
'periods': [{
'start': '1982',
'end': '1985',
'name': 'Early Era',
'characteristics': [...]
}]
}
"""
events = []
# Extract dates and events from all documents
for doc_id, doc in self.documents.items():
text = self._get_document_text(doc_id)
# Find dates (multiple patterns)
dates = self._extract_dates(text)
# Find events near dates
for date in dates:
event_text = self._extract_event_near_date(text, date)
entities = self._extract_entities_from_text(event_text)
events.append({
'date': date,
'event': event_text,
'entities': entities,
'source_doc': doc_id,
'confidence': self._calculate_confidence(event_text)
})
# Sort chronologically and group into periods
events_sorted = sorted(events, key=lambda x: x['date'])
periods = self._identify_periods(events_sorted)
return {
'events': events_sorted,
'periods': periods,
'entity_timeline': self._build_entity_timeline(events_sorted)
}
def get_entity_timeline(self, entity: str) -> dict:
"""Track an entity through time."""
return {
'entity': entity,
'first_mention': '1982-08',
'last_mention': '2025-12',
'mentions_by_year': {
'1982': 5,
'1983': 12,
...
},
'key_events': [...]
}
```
**MCP Tools:**
- `extract_timeline` - Build chronological timeline
- `get_entity_timeline` - Track entity over time
- `search_by_period` - Find documents from time period
- `get_technology_evolution` - See how tech evolved
---
### 5. Concept Network & Visualization ⭐⭐⭐⭐⭐
**What:** Build interactive concept maps and knowledge visualizations
**Algorithms:**
- **Force-Directed Layout** - Position nodes based on relationships
- **Hierarchical Layout** - Tree structure visualization
- **Topic Heatmaps** - Visualize topic distributions
- **Timeline Visualization** - Interactive chronological view
**Example Visualizations:**
```
1. Interactive Knowledge Graph
- Nodes: Entities (sized by importance)
- Edges: Relationships (thickness = strength)
- Colors: Entity types
- Interactive: Click to explore, zoom, filter
2. Topic Distribution Heatmap
- X-axis: Documents
- Y-axis: Topics
- Color: Topic strength
- Reveals document-topic patterns
3. Timeline Visualization
- X-axis: Time
- Y-axis: Categories (hardware, software, people)
- Events plotted chronologically
- Zoom and filter by entity
4. Cluster Dendrogram
- Hierarchical tree of document relationships
- Cut at different levels for varying granularity
- Interactive branch exploration
```
**Implementation:**
```python
def generate_knowledge_graph_viz(self, format: str = 'html') -> str:
"""
Generate interactive knowledge graph visualization.
Args:
format: 'html', 'json', 'graphml', 'gephi'
Returns:
Visualization data in requested format
"""
import plotly.graph_objects as go
# or: import networkx as nx; import pyvis
G = self.build_knowledge_graph()
# Calculate layout
pos = nx.spring_layout(G, k=0.5, iterations=50)
# Build Plotly figure
edge_trace = go.Scatter(...) # Draw edges
node_trace = go.Scatter(...) # Draw nodes
fig = go.Figure(data=[edge_trace, node_trace])
if format == 'html':
return fig.to_html()
elif format == 'json':
return fig.to_json()
def generate_topic_heatmap(self) -> str:
"""Generate topic distribution heatmap."""
topics = self.discover_topics()
# Create heatmap showing document-topic relationships
pass
def generate_timeline_viz(self) -> str:
"""Generate interactive timeline."""
timeline = self.extract_timeline()
# Create timeline visualization with plotly
pass
```
**Output Formats:**
- HTML (interactive, self-contained)
- JSON (for custom frontends)
- GraphML/GEXF (for Gephi, Cytoscape)
- SVG/PNG (static images)
---
### 6. Advanced Analytics Dashboard ⭐⭐⭐⭐
**What:** Comprehensive analytics interface in Streamlit GUI
**Features:**
- **Knowledge Graph Explorer** - Interactive graph visualization
- **Topic Discovery** - Topic modeling results and trends
- **Document Clusters** - Cluster visualization and navigation
- **Timeline View** - Chronological event browser
- **Entity Analytics** - Deep dive into entity networks
- **Insights Panel** - Automatically generated insights
**Screens:**
```
Tab 1: Knowledge Graph
- Interactive graph with filters
- Search and highlight entities
- Show/hide entity types
- Explore neighborhoods
Tab 2: Topics
- Topic list with top words
- Document-topic heatmap
- Topic evolution over time
- Search by topic
Tab 3: Clusters
- Cluster dendrogram
- Cluster summaries
- Document distribution
- Similarity matrix
Tab 4: Timeline
- Chronological event list
- Filter by entity/period
- Timeline visualization
- Entity evolution tracks
Tab 5: Insights
- Automatically generated insights
- "Hidden patterns discovered"
- Anomalies and outliers
- Recommendations
```
---
## 📊 Implementation Priority
### Phase 1: Foundation (v2.24.0) - 16-20 hours
1. **Knowledge Graph Analysis** - Core infrastructure
- Build NetworkX graph from existing entities
- Implement PageRank, community detection
- Add MCP tools for graph queries
- Cache graph for performance
2. **Basic Visualizations** - Initial displays
- Simple knowledge graph HTML export
- Entity network visualization
- Add to Streamlit GUI
### Phase 2: Discovery (v2.25.0) - 20-24 hours
3. **Topic Modeling** - Content analysis
- Implement LDA topic discovery
- Document-topic assignments
- Topic search and filtering
- MCP tools for topic queries
4. **Document Clustering** - Grouping
- K-means clustering on embeddings
- Cluster quality metrics
- Cluster summaries and navigation
- Recommendation engine
### Phase 3: Temporal (v2.26.0) - 16-20 hours
5. **Timeline Extraction** - Time-based analysis
- Date/event extraction
- Chronological ordering
- Entity timelines
- Period identification
6. **Advanced Visualizations** - Rich displays
- Interactive timeline
- Topic heatmaps
- Cluster dendrograms
- Export formats
### Phase 4: Integration (v2.27.0) - 12-16 hours
7. **Analytics Dashboard** - Unified interface
- Multi-tab Streamlit interface
- All visualizations integrated
- Automated insights
- Export capabilities
---
## 🎯 Benefits
### For Users
- **Discover Hidden Patterns** - Find connections not obvious from reading
- **Understand Topic Structure** - See major themes in documentation
- **Navigate by Similarity** - Find related documents automatically
- **Track Evolution** - See how topics/entities changed over time
- **Visual Exploration** - Interactive graphs and timelines
### For Researchers
- **Historical Analysis** - Track C64 history chronologically
- **Technology Evolution** - See how concepts developed
- **Community Detection** - Find related concepts/technologies
- **Gap Analysis** - Identify under-documented areas
### For Developers
- **API Access** - MCP tools for all analytics
- **Programmable** - Build custom queries and visualizations
- **Export Formats** - Use data in external tools
- **Integration Ready** - Connect to other systems
---
## 🛠️ Technical Requirements
### Dependencies
```bash
pip install networkx>=3.0
pip install scikit-learn>=1.3.0
pip install plotly>=5.0.0
pip install pyvis>=0.3.0
pip install hdbscan>=0.8.0
```
### Database Schema Additions
```sql
-- Store computed graph metrics
CREATE TABLE knowledge_graph_metrics (
computed_at TEXT,
metric_type TEXT,
entity_id TEXT,
value REAL,
metadata TEXT -- JSON
);
-- Store topic models
CREATE TABLE topic_models (
model_id TEXT PRIMARY KEY,
created_at TEXT,
num_topics INTEGER,
method TEXT, -- lda, nmf, bertopic
model_data BLOB, -- Pickled model
topics TEXT -- JSON of topic info
);
-- Store document clusters
CREATE TABLE document_clusters (
cluster_id INTEGER,
doc_id TEXT,
distance_to_centroid REAL,
cluster_label TEXT,
created_at TEXT
);
-- Store timeline events
CREATE TABLE timeline_events (
event_id TEXT PRIMARY KEY,
event_date TEXT,
event_text TEXT,
entities TEXT, -- JSON array
source_docs TEXT, -- JSON array
confidence REAL
);
```
---
## 🔬 Example Usage
```python
# Knowledge graph analysis
from server import KnowledgeBase
kb = KnowledgeBase()
# Analyze entity relationships
graph_stats = kb.analyze_knowledge_graph()
print(f"Most central entities: {graph_stats['pagerank'][:10]}")
print(f"Communities found: {len(graph_stats['communities'])}")
# Find connection between concepts
path = kb.find_entity_path("VIC-II", "SID chip")
print(f"Connection: {' → '.join(path['path'])}")
# Discover topics
topics = kb.discover_topics(num_topics=10)
for topic in topics['topics']:
print(f"Topic {topic['id']}: {', '.join(topic['top_words'][:5])}")
# Cluster documents
clusters = kb.cluster_documents(n_clusters=15)
for cluster in clusters['clusters'][:5]:
print(f"Cluster {cluster['id']}: {cluster['size']} docs")
# Extract timeline
timeline = kb.extract_timeline()
for event in timeline['events'][:10]:
print(f"{event['date']}: {event['event']}")
```
---
## 📚 Next Steps
1. **Review & Approve** - Confirm scope and priorities
2. **Phase 1 Implementation** - Start with knowledge graph
3. **Testing & Validation** - Verify insights are meaningful
4. **Documentation** - Update guides with new features
5. **Iteration** - Refine based on user feedback
---
**Version:** 2.23.16
**Status:** Design Proposal - Ready for Implementation
**Last Updated:** 2026-01-03
**Questions? Feedback?** This is a comprehensive proposal. We can:
- Implement all phases sequentially
- Start with specific algorithms that interest you most
- Adjust scope based on priorities
- Prototype specific features first
