Datasets:

AdvaithMagic
/

P-KG

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].