Graphify / sample_data.py
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CONCEPT_MAP_JSON = """
{
"central_node": "Artificial Intelligence (AI)",
"nodes": [
{
"id": "ml_fundamental",
"label": "Machine Learning",
"relationship": "is essential for",
"subnodes": [
{
"id": "dl_branch",
"label": "Deep Learning",
"relationship": "for example",
"subnodes": [
{
"id": "cnn_example",
"label": "CNNs",
"relationship": "for example"
},
{
"id": "rnn_example",
"label": "RNNs",
"relationship": "for example"
}
]
},
{
"id": "rl_branch",
"label": "Reinforcement Learning",
"relationship": "for example",
"subnodes": [
{
"id": "qlearning_example",
"label": "Q-Learning",
"relationship": "example"
},
{
"id": "pg_example",
"label": "Policy Gradients",
"relationship": "example"
}
]
}
]
},
{
"id": "ai_types",
"label": "Types",
"relationship": "formed by",
"subnodes": [
{
"id": "agi_type",
"label": "AGI",
"relationship": "this is",
"subnodes": [
{
"id": "strong_ai",
"label": "Strong AI",
"relationship": "provoked by",
"subnodes": [
{
"id": "human_intel",
"label": "Human-level Intel.",
"relationship": "of"
}
]
}
]
},
{
"id": "ani_type",
"label": "ANI",
"relationship": "this is",
"subnodes": [
{
"id": "weak_ai",
"label": "Weak AI",
"relationship": "provoked by",
"subnodes": [
{
"id": "narrow_tasks",
"label": "Narrow Tasks",
"relationship": "of"
}
]
}
]
}
]
},
{
"id": "ai_capabilities",
"label": "Capabilities",
"relationship": "change",
"subnodes": [
{
"id": "data_proc",
"label": "Data Processing",
"relationship": "can",
"subnodes": [
{
"id": "big_data",
"label": "Big Data",
"relationship": "as",
"subnodes": [
{
"id": "analysis_example",
"label": "Data Analysis",
"relationship": "example"
},
{
"id": "prediction_example",
"label": "Prediction",
"relationship": "example"
}
]
}
]
},
{
"id": "decision_making",
"label": "Decision Making",
"relationship": "can be",
"subnodes": [
{
"id": "automation",
"label": "Automation",
"relationship": "as",
"subnodes": [
{
"id": "robotics_example",
"label": "Robotics",
"relationship": "Example"},
{
"id": "autonomous_example",
"label": "Autonomous Vehicles",
"relationship": "of one"
}
]
}
]
},
{
"id": "problem_solving",
"label": "Problem Solving",
"relationship": "can",
"subnodes": [
{
"id": "optimization",
"label": "Optimization",
"relationship": "as is",
"subnodes": [
{
"id": "algorithms_example",
"label": "Algorithms",
"relationship": "for example"
}
]
}
]
}
]
}
]
}
"""
# JSON for Synoptic Chart (horizontal hierarchy) - AI related, 4 levels
SYNOPTIC_CHART_JSON = """
{
"central_node": "AI Project Lifecycle",
"nodes": [
{
"id": "phase1",
"label": "I. Problem Definition & Data Acquisition",
"relationship": "Starts with",
"subnodes": [
{
"id": "sub1_1",
"label": "1. Problem Formulation",
"relationship": "Involves",
"subnodes": [
{"id": "sub1_1_1", "label": "1.1. Identify Business Need", "relationship": "e.g."},
{"id": "sub1_1_2", "label": "1.2. Define KPIs", "relationship": "e.g."}
]
},
{
"id": "sub1_2",
"label": "2. Data Collection",
"relationship": "Followed by",
"subnodes": [
{"id": "sub1_2_1", "label": "2.1. Source Data", "relationship": "from"},
{"id": "sub1_2_2", "label": "2.2. Data Cleaning", "relationship": "includes"}
]
}
]
},
{
"id": "phase2",
"label": "II. Model Development",
"relationship": "Proceeds to",
"subnodes": [
{
"id": "sub2_1",
"label": "1. Feature Engineering",
"relationship": "Comprises",
"subnodes": [
{"id": "sub2_1_1", "label": "1.1. Feature Selection", "relationship": "e.g."},
{"id": "sub2_1_2", "label": "1.2. Feature Transformation", "relationship": "e.g."}
]
},
{
"id": "sub2_2",
"label": "2. Model Training",
"relationship": "Involves",
"subnodes": [
{"id": "sub2_2_1", "label": "2.1. Algorithm Selection", "relationship": "uses"},
{"id": "sub2_2_2", "label": "2.2. Hyperparameter Tuning", "relationship": "optimizes"}
]
}
]
},
{
"id": "phase3",
"label": "III. Evaluation & Deployment",
"relationship": "Culminates in",
"subnodes": [
{
"id": "sub3_1",
"label": "1. Model Evaluation",
"relationship": "Includes",
"subnodes": [
{"id": "sub3_1_1", "label": "1.1. Performance Metrics", "relationship": "measures"},
{"id": "sub3_1_2", "label": "1.2. Bias & Fairness Audits", "relationship": "ensures"}
]
},
{
"id": "sub3_2",
"label": "2. Deployment & Monitoring",
"relationship": "Requires",
"subnodes": [
{"id": "sub3_2_1", "label": "2.1. API/Integration Development", "relationship": "for"},
{"id": "sub3_2_2", "label": "2.2. Continuous Monitoring", "relationship": "ensures"}
]
}
]
}
]
}
"""
# JSON for Radial Diagram (central expansion) - AI related, 3 levels with 5->10 structure
RADIAL_DIAGRAM_JSON = """
{
"central_node": "AI Core Concepts & Domains",
"nodes": [
{
"id": "foundational_ml",
"label": "Foundational ML",
"relationship": "builds on",
"subnodes": [
{"id": "supervised_l", "label": "Supervised Learning", "relationship": "e.g."},
{"id": "unsupervised_l", "label": "Unsupervised Learning", "relationship": "e.g."}
]
},
{
"id": "dl_architectures",
"label": "Deep Learning Arch.",
"relationship": "evolved from",
"subnodes": [
{"id": "cnns_rad", "label": "CNNs", "relationship": "e.g."},
{"id": "rnns_rad", "label": "RNNs", "relationship": "e.g."}
]
},
{
"id": "major_applications",
"label": "Major AI Applications",
"relationship": "applied in",
"subnodes": [
{"id": "nlp_rad", "label": "Natural Language Processing", "relationship": "e.g."},
{"id": "cv_rad", "label": "Computer Vision", "relationship": "e.g."}
]
},
{
"id": "ethical_concerns",
"label": "Ethical AI Concerns",
"relationship": "addresses",
"subnodes": [
{"id": "fairness_rad", "label": "Fairness & Bias", "relationship": "e.g."},
{"id": "explainability", "label": "Explainability (XAI)", "relationship": "e.g."}
]
},
{
"id": "future_trends",
"label": "Future AI Trends",
"relationship": "looking at",
"subnodes": [
{"id": "agi_future", "label": "AGI Development", "relationship": "e.g."},
{"id": "quantum_ai", "label": "Quantum AI", "relationship": "e.g."}
]
}
]
}
"""