README.md

metadata

tags:
  - knowledge-graph
  - rdf
  - n-triples
  - sparql
  - semantic-web
  - stackoverflow
  - question-answering
pretty_name: StackOverflow Knowledge Graph - C++
license: cc-by-sa-4.0
size_categories:
  - 100M<n<1B

C++ StackOverflow Knowledge Graph Datasheet

1. What This Dataset Is

This dataset is the C++-specific RDF shard of the Stack2Graph StackOverflow Knowledge Graph. Each Hugging Face dataset repository contains exactly one language dataset.

It is optimized for graph-based retrieval and SPARQL analytics (not row-wise tabular training input).

It is used in the Stack2Graph paper for evaluating retrieval-based question answering and knowledge graph research on programming topics.

See the Stack2Graph repository for more details: https://github.com/tha-atlas/Stack2Graph

2. Repository Layout

schema.nt
c++/
  part0.nt
  part1.nt
  ...

• schema.nt: shared ontology and schema triples.
• c++/part*.nt: language-scoped instance triples serialized as N-Triples.

3. Data Model And Coverage

The graph links Stack Overflow entities and relations, including:

• questions
• answers
• comments
• tags
• vote aggregates
• question-to-question links

Coverage scope:

• records are retained when they match the Stack2Graph supported language-tag set
• this repository contains only the C++ shard

4. Recommended Preprocessing

1. Load schema.nt and all triples from c++/ into an RDF-capable store.
2. Map language triples to named graph http://stackoverflow.com/c++.
3. Keep schema triples available for graph-aware query planning.
4. Preserve attribution and license metadata during downstream export.

Example language-scoped query:

SELECT ?s ?p ?o
WHERE {
  GRAPH <http://stackoverflow.com/c++> {
    ?s ?p ?o
  }
}
LIMIT 100

5. Automatic Download And KG Setup

You do not need to rebuild triples from XML to use this dataset.

In the Stack2Graph repository, you can use the automation script experiment/load_hf_datasets_into_services.py to download dataset artifacts and prepare the knowledge graph service state automatically.

Typical workflow:

1. Clone and configure Stack2Graph (.env with HF token and service paths).
2. Start required local services:

docker compose up -d

1. Run the loader script:

python -m experiment.load_hf_datasets_into_services

For manual usage without automation, you can directly load schema.nt and all triples from c++/ into your RDF store.

6. Quality Notes And Caveats

• A Stack Overflow question may belong to multiple language shards when tagged with multiple languages.
• Vote information is represented as aggregates, not raw individual vote events.
• As with community-generated data, content may include noise, bias, and temporal drift.

7. Intended Use

• retrieval and question-answering systems
• RAG and hybrid retriever experiments
• knowledge graph benchmarking and diagnostics
• language-scoped developer tooling research

8. Limitations

• Not a complete mirror of all Stack Overflow content.
• Not all moderation context or full revision history is represented.
• Best used with graph infrastructure that supports named graphs and SPARQL.

9. Licensing And Attribution

This dataset inherits Stack Overflow source licensing and attribution requirements. Ensure compliant attribution and redistribution practices in all derived artifacts.

10. Suggested Citation

If you use this dataset, cite the Stack2Graph work:

• Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering