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license: cc-by-4.0
pretty_name: "The Missing Link: Knowledge Graph-Guided Discovery of Novel Prompt Compositions"
tags:
- knowledge-graph
- hypergraph
- prompt-engineering
- machine-learning
- owl
- rdf
size_categories:
- 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
The dataset uses a shared vocabulary defined in `ontology.ttl`. The core classes include:
- `Technique`: Prompt engineering techniques (e.g., Chain-of-Thought, ReAct).
- `AlgorithmicComponent`: Components containing execution, search, or retrieval logic.
- `PromptComponent`: Components defining prompt structures (examples, constraints).
- `DataFlow`: Components handling loop, feedback, or decomposition flows.
- `Task`: Evaluation datasets and tasks (e.g., GSM8K, HumanEval).
- `CognitiveCapability`: Intermediate capability nodes mapping Techniques to Tasks.
- `BenchmarkResult`: Specific model outputs on benchmarks.
---
## Directory Structure
```
/
├── README.md # This documentation file
├── metadata.json # Dataset metadata, versioning, and summary statistics
├── ontology.ttl # Shared OWL ontology defining classes and properties
├── flat_graph/ # Flat graph representation (binary relations)
│ ├── nodes.csv # Flat graph node table
│ ├── edges.csv # Flat graph edge table
│ ├── graph.json # Unified hierarchical JSON graph
│ ├── graph.ttl # RDF Turtle triples representation
│ └── node_features.npy # NumPy embedding matrix [num_nodes, 384] for nodes
└── hypergraph/ # Hypergraph representation (set-based techniques)
├── nodes.csv # Hypergraph node table (excludes Techniques)
├── hyperedges.csv # Hyperedges (Techniques) table
├── hyperedge_members.csv # Join table linking hyperedges to member nodes
├── hypergraph.json # Unified hierarchical JSON hypergraph
├── hypergraph.ttl # RDF Turtle reified hypergraph representation
└── node_features.npy # NumPy embedding matrix [num_nodes, 384] for nodes
```
---
## Dataset Statistics
### Flat Graph
- **Nodes**: 242
- **Edges**: 779
- **Node Embeddings**: `flat_graph/node_features.npy` has shape `[242, 384]` (384-dimensional SentenceTransformer embeddings generated using the `all-MiniLM-L6-v2` model).
### Hypergraph
- **Nodes (non-Technique members)**: 151
- **Hyperedges (Technique entities)**: 91
- **Node Embeddings**: `hypergraph/node_features.npy` has shape `[151, 384]`.
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