megagonlabs/cmdbench-nba

cmdbench-nba

CMDBench-NBA is a multimodal NBA datalake that consists of a Neo4j knowledge graph, a PostgreSQL relational database, and a MongoDB document collection. It is originally proposed in the CMDBench paper but has been updated with higher quality data (up to Feburary 2025).

Contact: yanlin@megagon.ai

Deploying the databases

First, clone the repository with git lfs installed.

# Make sure git-lfs is installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/datasets/megagonlabs/cmdbench-nba

Next, run the following commands under the root directory of the repository.

Starting cmdbench-nba-postgres

docker run -d \
  --name cmdbench-nba-postgres \
  -e POSTGRES_DB=nba_db \
  -e POSTGRES_HOST_AUTH_METHOD=trust \
  -p 22111:5432 \
  -v "$(pwd)/postgres/nba_db.sql:/docker-entrypoint-initdb.d/nba_db.sql" \
  postgres

Starting cmdbench-nba-neo4j

docker run -d \
  --name cmdbench-nba-neo4j \
  -p 22112:7687 \
  -p 22115:7474 \
  -v "$(pwd)/neo4j/nba_simplekg.json:/init/graph.json" \
  -e NEO4J_AUTH=neo4j/cmdbench \
  -e NEO4J_PLUGINS='["apoc", "graph-data-science"]' \
  megagonlabs/neo4j-with-loader:2.4

Starting cmdbench-nba-mongo

docker run -d \
  --name cmdbench-nba-mongo \
  -p 22113:27017 \
  -v "$(pwd)/mongo/nba-datalake.wiki-documents.json:/init/nba-datalake.wiki-documents.json" \
  -v "$(pwd)/mongo/init-nba-mongo.sh:/docker-entrypoint-initdb.d/init-nba-mongo.sh" \
  mongo mongod --noauth

Connection Info

Citation

If you use this dataset in your work, please cite the following papers:

@article{feng2024cypherbench,
  title={CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era},
  author={Feng, Yanlin and Papicchio, Simone and Rahman, Sajjadur},
  journal={arXiv preprint arXiv:2412.18702},
  year={2024}
}

@inproceedings{feng2024cmdbench,
  title={CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems},
  author={Feng, Yanlin and Rahman, Sajjadur and Feng, Aaron and Chen, Vincent and Kandogan, Eser},
  booktitle={Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI},
  pages={16--25},
  year={2024}
}

Attribution

This dataset is built upon the following data sources:

• —Kaggle NBA DB (license: CC-BY-SA-4.0)
• —Wikipedia (license: CC-BY-SA-3.0)
• —Wikidata (license: CC0)

Disclosure

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