semantica/docs/cookbook/advanced/Complete_Visualization_Suite.ipynb

Complete Visualization Suite

Overview

Comprehensive visualization capabilities: visualize knowledge graphs, embeddings, quality metrics, analytics, and temporal data.

In [ ]:

from semantica.visualization import (
    KGVisualizer,
    EmbeddingVisualizer,
    QualityVisualizer,
    AnalyticsVisualizer,
    TemporalVisualizer
)
from semantica.kg import GraphBuilder, GraphAnalyzer
from semantica.embeddings import EmbeddingGenerator
from semantica.kg_qa import KGQualityAssessor
import numpy as np

Step 1: Create Sample Knowledge Graph

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builder = GraphBuilder()

entities = [
    {"id": "e1", "type": "Person", "name": "Alice", "properties": {"age": 30}},
    {"id": "e2", "type": "Person", "name": "Bob", "properties": {"age": 35}},
    {"id": "e3", "type": "Organization", "name": "Tech Corp", "properties": {"founded": 2010}},
    {"id": "e4", "type": "Location", "name": "San Francisco", "properties": {"country": "USA"}},
]

relationships = [
    {"source": "e1", "target": "e2", "type": "knows", "properties": {"since": 2020}},
    {"source": "e1", "target": "e3", "type": "works_for", "properties": {"role": "Engineer"}},
    {"source": "e3", "target": "e4", "type": "located_in", "properties": {}},
]

knowledge_graph = builder.build(entities, relationships)

Step 2: Knowledge Graph Visualization

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kg_visualizer = KGVisualizer()
kg_visualizer.visualize(knowledge_graph, layout="spring", show_labels=True)

Step 3: Generate Embeddings and Visualize

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embedding_generator = EmbeddingGenerator()
texts = [entity.get("name", "") for entity in entities]
embeddings = embedding_generator.generate(texts)

labels = [entity.get("type", "Unknown") for entity in entities]

embedding_visualizer = EmbeddingVisualizer()
embedding_visualizer.visualize_tsne(embeddings, labels, title="Entity Embeddings Visualization")

Step 4: Quality Metrics Visualization

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quality_assessor = KGQualityAssessor()
quality_metrics = quality_assessor.assess(knowledge_graph)

quality_visualizer = QualityVisualizer()
quality_visualizer.visualize_metrics(quality_metrics, title="Knowledge Graph Quality Metrics")

Step 5: Graph Analytics Visualization

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graph_analyzer = GraphAnalyzer()

centrality_results = graph_analyzer.calculate_centrality(
    knowledge_graph, 
    centrality_type="degree"
)

centrality_scores = {}
if centrality_results and "centrality_measures" in centrality_results:
    degree_centrality = centrality_results["centrality_measures"].get("degree", {})
    if isinstance(degree_centrality, dict) and "centrality" in degree_centrality:
        centrality_scores = degree_centrality["centrality"]
    elif isinstance(degree_centrality, dict):
        centrality_scores = degree_centrality

communities_result = graph_analyzer.detect_communities(
    knowledge_graph, 
    algorithm="louvain"
)

communities = []
community_dict = {}
if communities_result and "communities" in communities_result:
    communities_data = communities_result["communities"]
    if isinstance(communities_data, list):
        communities = communities_data
        for idx, community in enumerate(communities):
            if isinstance(community, list):
                for node in community:
                    community_dict[node] = idx
            elif isinstance(community, dict) and "nodes" in community:
                for node in community["nodes"]:
                    community_dict[node] = idx

analytics_visualizer = AnalyticsVisualizer()
analytics_visualizer.visualize_centrality(centrality_scores, title="Node Centrality Scores")

if community_dict:
    analytics_visualizer.visualize_communities(
        knowledge_graph, 
        community_dict, 
        title="Community Detection"
    )

Step 6: Temporal Data Visualization

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temporal_kg = {
    "entities": entities,
    "relationships": relationships,
    "timestamps": {
        "e1": [2020, 2021, 2022],
        "e2": [2020, 2021],
        "e3": [2010, 2015, 2020, 2022],
    }
}

entity_history = {
    "e1": [
        {"timestamp": 2020, "properties": {"age": 28}},
        {"timestamp": 2021, "properties": {"age": 29}},
        {"timestamp": 2022, "properties": {"age": 30}},
    ]
}

temporal_visualizer = TemporalVisualizer()
temporal_visualizer.visualize_timeline(temporal_kg, title="Temporal Knowledge Graph Timeline")
temporal_visualizer.visualize_evolution(entity_history, entity_id="e1", title="Entity Evolution")

Summary

All visualization types demonstrated:

• Knowledge Graph Visualization
• Embedding Visualization (t-SNE)
• Quality Metrics Visualization
• Graph Analytics Visualization (Centrality & Communities)
• Temporal Data Visualization (Timeline & Evolution)

In [ ]:

print("Complete Visualization Suite")
print("All visualizations generated successfully")