| ---
|
| license: cc-by-4.0
|
| task_categories:
|
| - question-answering
|
| - text-retrieval
|
| language:
|
| - en
|
| tags:
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| - biomedical
|
| - pubmed
|
| - knowledge-graph
|
| - graphrag
|
| - neo4j
|
| - faiss
|
| - embeddings
|
| size_categories:
|
| - 10M<n<100M
|
| pretty_name: PubMed GraphRAG
|
| ---
|
|
|
| # PubMed-GraphRAG Dataset
|
|
|
| A large-scale biomedical knowledge graph dataset for Graph-based Retrieval-Augmented Generation (GraphRAG).
|
|
|
| ## Dataset Overview
|
|
|
| | Component | Size | Description |
|
| |-----------|------|-------------|
|
| | Papers | 12.5M | PubMed abstracts (2000-2024) with title, abstract, DOI |
|
| | Entities | 7.5M | Genes, Diseases, Chemicals, Species, Mutations, Cell Lines |
|
| | Relationships | 266M+ | MENTIONED_IN, TREAT, ASSOCIATE, CAUSE, INHIBIT, etc. |
|
| | Embeddings | 12.5M vectors | MedCPT, BiCA, MedTE (768-dim) |
|
|
|
| ## Dataset Structure
|
|
|
| ```
|
| pubmed-graphrag-data/
|
| ├── graph_ready/ # Papers & major MENTIONED_IN relationships
|
| │ ├── papers.csv # 12.5M papers (pmid, title, abstract, year, doi)
|
| │ ├── mentioned_in_gene.csv # Gene-Paper relationships
|
| │ ├── mentioned_in_disease.csv # Disease-Paper relationships
|
| │ ├── mentioned_in_chemical.csv # Chemical-Paper relationships
|
| │ ├── entity_relations.csv # Entity-Entity semantic relationships
|
| │ └── complete_papers_with_metadata.jsonl # Full data in JSONL format
|
| │
|
| ├── neo4j_csv/ # Entity definitions & additional relationships
|
| │ ├── gene_entities.csv # 3.8M genes (entity_id, mention)
|
| │ ├── disease_entities.csv # 12K diseases
|
| │ ├── chemical_entities.csv # 120K chemicals
|
| │ ├── species_entities.csv # 359K species
|
| │ ├── mutation_entities.csv # 3.2M mutations
|
| │ ├── cellline_entities.csv # 63K cell lines
|
| │ ├── mentioned_in_species.csv # Species-Paper relationships
|
| │ ├── mentioned_in_mutation.csv # Mutation-Paper relationships
|
| │ ├── mentioned_in_cellline.csv # CellLine-Paper relationships
|
| │ └── rel_*.csv # 32 semantic relationship files (TREAT, CAUSE, etc.)
|
| │
|
| ├── medcpt/ # MedCPT embeddings (recommended)
|
| │ ├── embeddings_000000.npz # Each NPZ contains 'embeddings' and 'pmids' arrays
|
| │ ├── ...
|
| │ └── embeddings_000251.npz
|
| │
|
| ├── bica_base/ # BiCA embeddings
|
| │ └── embeddings_*.npz
|
| │
|
| └── medte/ # MedTE embeddings
|
| └── embeddings_*.npz
|
| ```
|
|
|
| ## Quick Start: Building the Knowledge Graph
|
|
|
| ### Prerequisites
|
| - Neo4j 5.x (Community Edition works)
|
| - Python 3.8+
|
| - ~50GB disk space
|
| - 16GB+ RAM recommended
|
|
|
| ### Step 1: Download all CSV files
|
|
|
| ```python
|
| from huggingface_hub import snapshot_download
|
|
|
| # Download all CSVs
|
| snapshot_download(
|
| repo_id="dannyroxas/pubmed-graphrag-data",
|
| repo_type="dataset",
|
| local_dir="./pubmed-data",
|
| allow_patterns=["graph_ready/*.csv", "neo4j_csv/*.csv"]
|
| )
|
| ```
|
|
|
| ### Step 2: Import to Neo4j
|
|
|
| ```bash
|
| # Stop Neo4j
|
| sudo systemctl stop neo4j
|
|
|
| # Import (adjust paths as needed)
|
| cd ./pubmed-data
|
|
|
| sudo neo4j-admin database import full \
|
| --nodes=Paper="graph_ready/papers.csv" \
|
| --nodes=Gene="neo4j_csv/gene_entities.csv" \
|
| --nodes=Disease="neo4j_csv/disease_entities.csv" \
|
| --nodes=Chemical="neo4j_csv/chemical_entities.csv" \
|
| --nodes=Species="neo4j_csv/species_entities.csv" \
|
| --nodes=Mutation="neo4j_csv/mutation_entities.csv" \
|
| --nodes=CellLine="neo4j_csv/cellline_entities.csv" \
|
| --relationships=MENTIONED_IN="graph_ready/mentioned_in_gene.csv" \
|
| --relationships=MENTIONED_IN="graph_ready/mentioned_in_disease.csv" \
|
| --relationships=MENTIONED_IN="graph_ready/mentioned_in_chemical.csv" \
|
| --relationships=MENTIONED_IN="neo4j_csv/mentioned_in_species.csv" \
|
| --relationships=MENTIONED_IN="neo4j_csv/mentioned_in_mutation.csv" \
|
| --relationships=MENTIONED_IN="neo4j_csv/mentioned_in_cellline.csv" \
|
| --relationships=ASSOCIATE="neo4j_csv/rel_*_ASSOCIATE.csv" \
|
| --relationships=TREAT="neo4j_csv/rel_*_TREAT.csv" \
|
| --relationships=CAUSE="neo4j_csv/rel_*_CAUSE.csv" \
|
| --relationships=INHIBIT="neo4j_csv/rel_*_INHIBIT.csv" \
|
| --relationships=STIMULATE="neo4j_csv/rel_*_STIMULATE.csv" \
|
| --relationships=INTERACT="neo4j_csv/rel_*_INTERACT.csv" \
|
| --relationships=POSITIVE_CORRELATE="neo4j_csv/rel_*_POSITIVE_CORRELATE.csv" \
|
| --relationships=NEGATIVE_CORRELATE="neo4j_csv/rel_*_NEGATIVE_CORRELATE.csv" \
|
| --relationships=COTREAT="neo4j_csv/rel_*_COTREAT.csv" \
|
| --relationships=COMPARE="neo4j_csv/rel_*_COMPARE.csv" \
|
| --relationships=PREVENT="neo4j_csv/rel_*_PREVENT.csv" \
|
| --relationships=DRUG_INTERACT="neo4j_csv/rel_*_DRUG_INTERACT.csv" \
|
| --skip-bad-relationships \
|
| --skip-duplicate-nodes \
|
| neo4j
|
|
|
| # Fix permissions and start
|
| sudo chown -R neo4j:neo4j /var/lib/neo4j/data
|
| sudo systemctl start neo4j
|
| ```
|
|
|
| ### Step 3: Create indexes
|
|
|
| ```cypher
|
| CREATE INDEX paper_pmid FOR (p:Paper) ON (p.pmid);
|
| CREATE INDEX gene_mention FOR (g:Gene) ON (g.mention);
|
| CREATE INDEX gene_id FOR (g:Gene) ON (g.entity_id);
|
| CREATE INDEX disease_mention FOR (d:Disease) ON (d.mention);
|
| CREATE INDEX chemical_mention FOR (c:Chemical) ON (c.mention);
|
| CREATE INDEX species_mention FOR (s:Species) ON (s.mention);
|
| CREATE INDEX mutation_mention FOR (m:Mutation) ON (m.mention);
|
| ```
|
|
|
| ## Quick Start: Building FAISS Index
|
|
|
| ### Step 1: Download embeddings
|
|
|
| ```python
|
| from huggingface_hub import snapshot_download
|
|
|
| # Download MedCPT embeddings (recommended for biomedical)
|
| snapshot_download(
|
| repo_id="dannyroxas/pubmed-graphrag-data",
|
| repo_type="dataset",
|
| local_dir="./pubmed-data",
|
| allow_patterns="medcpt/*.npz"
|
| )
|
| ```
|
|
|
| ### Step 2: Build IVF-PQ index
|
|
|
| ```python
|
| import numpy as np
|
| import faiss
|
| from pathlib import Path
|
| from tqdm import tqdm
|
|
|
| # Configuration
|
| NPZ_DIR = Path("./pubmed-data/medcpt")
|
| OUTPUT_DIR = Path("./faiss_index")
|
| OUTPUT_DIR.mkdir(exist_ok=True)
|
|
|
| NLIST = 4096 # Number of clusters
|
| M = 96 # Subquantizers (768/96 = 8 dims each)
|
| NBITS = 8 # Bits per code
|
| TRAIN_SIZE = 500000
|
|
|
| # Get all NPZ files
|
| npz_files = sorted(NPZ_DIR.glob("embeddings_*.npz"))
|
| print(f"Found {len(npz_files)} embedding files")
|
|
|
| # Step 1: Sample vectors for training
|
| print("Sampling vectors for training...")
|
| train_vectors = []
|
| for npz_file in tqdm(npz_files[:50], desc="Sampling"):
|
| data = np.load(npz_file)
|
| emb = data["embeddings"].astype(np.float32)
|
| idx = np.random.choice(len(emb), min(10000, len(emb)), replace=False)
|
| train_vectors.append(emb[idx])
|
|
|
| train_vectors = np.vstack(train_vectors)[:TRAIN_SIZE]
|
| faiss.normalize_L2(train_vectors)
|
| dim = train_vectors.shape[1]
|
| print(f"Training set: {len(train_vectors):,} vectors, dim={dim}")
|
|
|
| # Step 2: Create and train index
|
| print("Training IVF-PQ index...")
|
| quantizer = faiss.IndexFlatIP(dim)
|
| index = faiss.IndexIVFPQ(quantizer, dim, NLIST, M, NBITS, faiss.METRIC_INNER_PRODUCT)
|
| index.train(train_vectors)
|
| index.nprobe = 128
|
| del train_vectors
|
|
|
| # Step 3: Add all vectors
|
| print("Adding all vectors...")
|
| all_pmids = []
|
| for npz_file in tqdm(npz_files, desc="Adding"):
|
| data = np.load(npz_file)
|
| emb = data["embeddings"].astype(np.float32)
|
| pmids = data["pmids"]
|
| faiss.normalize_L2(emb)
|
| index.add(emb)
|
| all_pmids.extend(pmids.tolist())
|
|
|
| # Step 4: Save
|
| print("Saving index...")
|
| faiss.write_index(index, str(OUTPUT_DIR / "index.faiss"))
|
| np.save(OUTPUT_DIR / "pmids.npy", np.array(all_pmids))
|
|
|
| print(f"\nDone!")
|
| print(f" Index: {index.ntotal:,} vectors")
|
| print(f" Size: ~{index.ntotal * M / 1e9:.1f} GB (compressed)")
|
| ```
|
|
|
| ## NPZ File Format
|
|
|
| Each `.npz` file contains:
|
| - `embeddings`: numpy array of shape `(N, 768)` - float32 vectors
|
| - `pmids`: numpy array of shape `(N,)` - corresponding PubMed IDs
|
|
|
| ```python
|
| import numpy as np
|
|
|
| data = np.load("medcpt/embeddings_000000.npz")
|
| print(data["embeddings"].shape) # (50000, 768)
|
| print(data["pmids"].shape) # (50000,)
|
| ```
|
|
|
| ## Entity Types
|
|
|
| | Type | Count | ID Format | Example |
|
| |------|-------|-----------|---------|
|
| | Gene | 3.8M | NCBI Gene ID | `348` (APOE) |
|
| | Disease | 12K | MeSH ID | `MESH:D000544` (Alzheimer Disease) |
|
| | Chemical | 120K | MeSH ID | `MESH:D001241` (Aspirin) |
|
| | Species | 359K | NCBI Taxonomy | `9606` (Homo sapiens) |
|
| | Mutation | 3.2M | dbSNP/HGVS | `rs121912438` |
|
| | Cell Line | 63K | Cellosaurus | `CVCL_0030` |
|
|
|
| ## Relationship Types
|
|
|
| | Type | Count | Example |
|
| |------|-------|---------|
|
| | MENTIONED_IN | 248M | (Gene)-[:MENTIONED_IN]->(Paper) |
|
| | ASSOCIATE | 9.2M | (Gene)-[:ASSOCIATE]->(Disease) |
|
| | TREAT | 3.1M | (Chemical)-[:TREAT]->(Disease) |
|
| | POSITIVE_CORRELATE | 1.8M | (Chemical)-[:POSITIVE_CORRELATE]->(Gene) |
|
| | NEGATIVE_CORRELATE | 1.8M | (Chemical)-[:NEGATIVE_CORRELATE]->(Gene) |
|
| | CAUSE | 1.3M | (Mutation)-[:CAUSE]->(Disease) |
|
| | STIMULATE | 388K | (Chemical)-[:STIMULATE]->(Gene) |
|
| | INHIBIT | 307K | (Chemical)-[:INHIBIT]->(Gene) |
|
| | COTREAT | 237K | (Chemical)-[:COTREAT]->(Chemical) |
|
| | COMPARE | 208K | (Chemical)-[:COMPARE]->(Chemical) |
|
| | INTERACT | 123K | (Gene)-[:INTERACT]->(Gene) |
|
| | PREVENT | ~5K | (Mutation)-[:PREVENT]->(Disease) |
|
|
|
| ## Embedding Models
|
|
|
| | Model | HuggingFace | Dimensions | Recommended For |
|
| |-------|-------------|------------|-----------------|
|
| | MedCPT | [`ncbi/MedCPT-Article-Encoder`](https://huggingface.co/ncbi/MedCPT-Article-Encoder) | 768 | Medical literature search |
|
| | BiCA | [`bisectgroup/BiCA-base`](https://huggingface.co/bisectgroup/BiCA-base) | 768 | Biomedical contrastive learning |
|
| | MedTE | [`MohammadKhodadad/MedTE`](https://huggingface.co/MohammadKhodadad/MedTE) | 768 | Medical text embeddings |
|
|
|
| ## Sample Neo4j Queries
|
|
|
| ### Find genes associated with Alzheimer's disease
|
| ```cypher
|
| MATCH (d:Disease)-[:ASSOCIATE]-(g:Gene)
|
| WHERE d.mention CONTAINS "Alzheimer"
|
| RETURN g.mention AS gene, g.entity_id AS ncbi_id
|
| LIMIT 20
|
| ```
|
|
|
| ### Find drugs that treat diabetes
|
| ```cypher
|
| MATCH (c:Chemical)-[:TREAT]->(d:Disease)
|
| WHERE d.mention CONTAINS "Diabetes"
|
| RETURN c.mention AS drug, count(*) AS evidence_count
|
| ORDER BY evidence_count DESC
|
| LIMIT 10
|
| ```
|
|
|
| ### Multi-hop: Find genes related to drugs treating a disease
|
| ```cypher
|
| MATCH (c:Chemical)-[:TREAT]->(d:Disease)
|
| WHERE d.mention CONTAINS "Breast"
|
| MATCH (c)-[r:ASSOCIATE|INHIBIT|STIMULATE]-(g:Gene)
|
| RETURN DISTINCT g.mention AS gene, c.mention AS drug, type(r) AS relationship
|
| LIMIT 25
|
| ```
|
|
|
| ### Find papers discussing a specific gene
|
| ```cypher
|
| MATCH (g:Gene {mention: "BRCA1"})-[:MENTIONED_IN]->(p:Paper)
|
| RETURN p.pmid, p.title, p.year
|
| ORDER BY p.year DESC
|
| LIMIT 10
|
| ```
|
|
|
| ## Data Sources
|
|
|
| | Source | Description | Link |
|
| |--------|-------------|------|
|
| | **PubMed** | 12.5M biomedical abstracts (2000-2024) | [pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/) |
|
| | **PubTator3** | Pre-extracted entities & relationships | [ncbi.nlm.nih.gov/research/pubtator3](https://www.ncbi.nlm.nih.gov/research/pubtator3/) |
|
| | **NCBI Gene** | Gene symbols and metadata | [ncbi.nlm.nih.gov/gene](https://www.ncbi.nlm.nih.gov/gene/) |
|
| | **MeSH** | Medical Subject Headings (diseases, chemicals) | [meshb.nlm.nih.gov](https://meshb.nlm.nih.gov/) |
|
| | **NCBI Taxonomy** | Species classification | [ncbi.nlm.nih.gov/taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) |
|
| | **Cellosaurus** | Cell line database | [cellosaurus.org](https://www.cellosaurus.org/) |
|
| | **dbSNP** | Mutation/variant identifiers | [ncbi.nlm.nih.gov/snp](https://www.ncbi.nlm.nih.gov/snp/) |
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @dataset{pubmed_graphrag_2025,
|
| author = {Roxas, Danny},
|
| title = {PubMed-GraphRAG: A Large-scale Biomedical Knowledge Graph Dataset},
|
| year = {2025},
|
| publisher = {Hugging Face},
|
| howpublished = {\url{https://huggingface.co/datasets/dannyroxas/pubmed-graphrag-data}}
|
| }
|
| ```
|
|
|
| ## License
|
|
|
| This dataset is derived from publicly available biomedical databases:
|
| - [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts (NLM/NCBI)
|
| - [PubTator3](https://www.ncbi.nlm.nih.gov/research/pubtator3/) annotations (NCBI)
|
| - [MeSH](https://meshb.nlm.nih.gov/) descriptors (NLM)
|
| - [NCBI Gene](https://www.ncbi.nlm.nih.gov/gene/) and [Taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) databases
|
|
|
| Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) for academic research purposes.
|
|
|
| ## Acknowledgments
|
|
|
| - [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/)
|
| - [National Library of Medicine (NLM)](https://www.nlm.nih.gov/)
|
| - [Meta AI - FAISS](https://github.com/facebookresearch/faiss)
|
| - [Neo4j](https://neo4j.com/)
|
|
|
