| ---
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| license: cc-by-4.0
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| pretty_name: "The Missing Link: Knowledge Graph-Guided Discovery of Novel Prompt Compositions"
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| tags:
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| - knowledge-graph
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| - hypergraph
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| - prompt-engineering
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| - machine-learning
|
| - owl
|
| - rdf
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| size_categories:
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| - n<1K
|
| ---
|
|
|
| # P-KG - From "The Missing Link: Knowledge Graph-Guided Discovery of Novel Prompt Compositions (A.S. Kumar et al., 2026)"
|
|
|
| This repository contains the knowledge graph and hypergraph utilised in the work "The Missing Link: Knowledge Graph-Guided Discovery of Novel Prompt Compositions (A.S. Kumar et al., 2026)".
|
|
|
| ## Licensing
|
| This data is released under the **Creative Commons Attribution 4.0 International (CC-BY-4.0)** license. You are free to share, copy, and adapt this dataset for any purpose, including commercially, provided you give appropriate credit.
|
|
|
| ## Data Schema & Ontologies
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| The dataset uses a shared vocabulary defined in `ontology.ttl`. The core classes include:
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| - `Technique`: Prompt engineering techniques (e.g., Chain-of-Thought, ReAct).
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| - `AlgorithmicComponent`: Components containing execution, search, or retrieval logic.
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| - `PromptComponent`: Components defining prompt structures (examples, constraints).
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| - `DataFlow`: Components handling loop, feedback, or decomposition flows.
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| - `Task`: Evaluation datasets and tasks (e.g., GSM8K, HumanEval).
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| - `CognitiveCapability`: Intermediate capability nodes mapping Techniques to Tasks.
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| - `BenchmarkResult`: Specific model outputs on benchmarks.
|
|
|
| ---
|
|
|
| ## Directory Structure
|
|
|
| ```
|
| /
|
| ├── README.md # This documentation file
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| ├── metadata.json # Dataset metadata, versioning, and summary statistics
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| ├── ontology.ttl # Shared OWL ontology defining classes and properties
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| ├── flat_graph/ # Flat graph representation (binary relations)
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| │ ├── nodes.csv # Flat graph node table
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| │ ├── edges.csv # Flat graph edge table
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| │ ├── graph.json # Unified hierarchical JSON graph
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| │ ├── graph.ttl # RDF Turtle triples representation
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| │ └── node_features.npy # NumPy embedding matrix [num_nodes, 384] for nodes
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| └── hypergraph/ # Hypergraph representation (set-based techniques)
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| ├── nodes.csv # Hypergraph node table (excludes Techniques)
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| ├── hyperedges.csv # Hyperedges (Techniques) table
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| ├── hyperedge_members.csv # Join table linking hyperedges to member nodes
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| ├── hypergraph.json # Unified hierarchical JSON hypergraph
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| ├── hypergraph.ttl # RDF Turtle reified hypergraph representation
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| └── node_features.npy # NumPy embedding matrix [num_nodes, 384] for nodes
|
| ```
|
|
|
| ---
|
|
|
| ## Dataset Statistics
|
|
|
| ### Flat Graph
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| - **Nodes**: 242
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| - **Edges**: 779
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| - **Node Embeddings**: `flat_graph/node_features.npy` has shape `[242, 384]` (384-dimensional SentenceTransformer embeddings generated using the `all-MiniLM-L6-v2` model).
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|
|
| ### Hypergraph
|
| - **Nodes (non-Technique members)**: 151
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| - **Hyperedges (Technique entities)**: 91
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| - **Node Embeddings**: `hypergraph/node_features.npy` has shape `[151, 384]`.
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|
|
|
|
