---
pretty_name: FACTPROP — Connected Factual Knowledge Graph
language:
  - en
license: cc-by-4.0
size_categories:
  - 100K<n<1M
tags:
  - knowledge-graph
  - knowledge-updating
  - graph-analysis
  - factual-knowledge
  - question-answering
  - retrieval
  - factprop
configs:
  - config_name: knowledge_graph
    default: true
    data_files:
      - split: knowledge_edges
        path: data/knowledge_edges.parquet
  - config_name: entity_index
    data_files:
      - split: curated
        path: data/entities_curated.parquet
---

# FACTPROP

**Popular Knowledge Propagates More Errors in LLM Knowledge Updating**

[Project page & demo](https://factprop.github.io/FACTPROP/) · [Research code](https://github.com/factprop/FACTPROP) · [Graph schema](GRAPH.md)

## What is FACTPROP?

FACTPROP is a connected factual knowledge graph for studying how knowledge is structured and how factual updates propagate through large language models. The released builder generates factual candidates with an LLM and validates supported relations against Wikidata. Project materials describe the candidates as Wikipedia-derived, but the released pipeline does not ingest Wikipedia article text.

The release provides two ways to use the same graph:

1. **`knowledge_edges` (recommended):** a safe, columnar Parquet view of every forward factual connection, loadable without Python pickle.
2. **`factprop_graph_v1.pkl` (canonical):** the unchanged NetworkX checkpoint containing the complete graph and construction metadata.

The default Dataset Viewer shows each knowledge connection as:

```text
subject ── relation ──▶ object_node_label
```

For example, an edge can also carry a natural-language question, a surface statement, construction-time confidence, and connectivity statistics.

## At a glance

- **100,015 graph nodes**
- **357,205 forward factual edges**
- **75,357 auxiliary inverse traversal edges**
- **432,562 stored directed edges in the canonical checkpoint**
- **31 forward relation identifiers** in `knowledge_edges`, plus **8 inverse-only identifiers** in the checkpoint (**39 stored relation types total**)
- Natural-language `question` and `surface` fields on graph edges where available
- Wikidata QIDs where mappings are available
- Forward-edge in-degree and out-degree for connectivity analysis

The default Parquet split contains all 357,205 forward edges. It is not a sample and is not a predefined train/test split.

## Which artifact should I use?

| Goal | Recommended artifact | What it contains |
| --- | --- | --- |
| Browse or analyze factual connections | `knowledge_graph/knowledge_edges` | All forward `subject → relation → object` edges and selected metadata |
| Use a DataFrame, Arrow, DuckDB, or Hugging Face Datasets | `data/knowledge_edges.parquet` | Compact tabular representation of forward graph topology |
| Inspect mapped, readable entities by popularity | `entity_index/curated` | Entity labels, QIDs, and forward object in-degree |
| Reproduce exact graph traversal or inspect evidence | `factprop_graph_v1.pkl` | Canonical NetworkX graph, inverse edges, evidence, and construction state |
| Verify files or pin a release | `SHA256SUMS` and a Hub revision | Published checksums and immutable commit history |

### What does “download the dataset” mean?

- `load_dataset(..., split="knowledge_edges")` downloads the complete **forward connection table**.
- `hf download factprop/FACTPROP --repo-type dataset` downloads the **entire release**, including the canonical graph checkpoint.
- `load_dataset(..., split="curated")` downloads only the curated entity summary; it is not the complete graph.

If your goal is to obtain all knowledge connections in an easy-to-use format, use `knowledge_edges`. If you also need inverse traversal edges, stored evidence, or construction metadata, download `factprop_graph_v1.pkl`.

## Data model

### Nodes

Node identifiers are graph-label strings. A node can represent:

- A mapped entity with a Wikidata QID.
- An unmapped entity label.
- A literal value such as a date.

The canonical checkpoint stores `qid` and `qid_status` as node attributes.

### Forward knowledge edges

Each forward edge represents one stored factual connection:

```text
(subject, relation, object_node_label)
```

The Parquet representation includes:

| Field | Type | Meaning |
| --- | --- | --- |
| `edge_id` | integer | Sequential table row ID starting at zero; not a graph-node ID |
| `subject` | string | Source node label |
| `subject_qid` | string or null | Subject Wikidata QID where available |
| `relation` | string | FACTPROP relation identifier |
| `object_node_label` | string | Canonical target-node label; use this field for display, filtering, and topology reconstruction |
| `object_qid` | string or null | Object Wikidata QID where available |
| `object_type` | string | `entity`, `literal`, or `date_literal` |
| `question` | string or null | Natural-language question associated with the fact |
| `surface` | string or null | Natural-language statement expressing the fact |
| `group` | string or null | Construction category such as Person, Work, or Event |
| `confidence` | float or null | Construction-time confidence; not a calibrated probability |
| `subject_forward_out_degree` | integer | Number of forward facts originating from the subject |
| `object_forward_in_degree` | integer | Number of forward facts pointing to the object |

The table is sorted by descending `object_forward_in_degree`, making highly connected objects easy to inspect. `object_node_label` is the single target-node field and exactly matches the canonical checkpoint label.

### Auxiliary inverse edges

The checkpoint also contains 75,357 edges whose `is_inverse` attribute is `true`. These support reverse traversal but must not be counted as additional original facts. They are excluded from `knowledge_edges`.

### Structural popularity

FACTPROP defines structural popularity for an object node `o` as:

```text
number of incoming edges to o where is_inverse is not true
```

This is the `object_forward_in_degree` field in the edge table and `forward_object_in_degree` in the entity index.

It measures connectivity inside this graph version. It does **not** measure web traffic, public familiarity, cultural importance, or the error probability of a particular model.

For example:

- `Apple Inc.` / `Q312`: forward object in-degree **467**
- `Apple` / `Q89`: forward object in-degree **4**

These are separate graph nodes.

## Quick start: load every forward knowledge connection

Install the tabular dependencies:

```bash
python -m pip install datasets pyarrow
```

Load the complete forward graph table:

```python
from datasets import load_dataset

edges = load_dataset(
    "factprop/FACTPROP",
    "knowledge_graph",
    split="knowledge_edges",
)

print(edges.num_rows)       # 357205
print(edges.column_names)
```

Inspect one connection:

```python
row = edges[0]
print(f'{row["subject"]} --{row["relation"]}--> {row["object_node_label"]}')
print("Question:", row["question"])
print("Statement:", row["surface"])
print("Object connectivity:", row["object_forward_in_degree"])
```

Pin the data revision for a reproducible experiment:

```python
edges = load_dataset(
    "factprop/FACTPROP",
    "knowledge_graph",
    split="knowledge_edges",
    revision="<commit-id>",
)
```

## Common recipes

### Find all facts involving an entity

```python
entity = "Apple Inc."

connected = edges.filter(
    lambda row: (
        row["subject"] == entity or row["object_node_label"] == entity
    )
)

print(
    connected.select_columns(
        ["subject", "relation", "object_node_label"]
    )[:20]
)
```

For incoming facts only:

```python
incoming = edges.filter(lambda row: row["object_node_label"] == entity)
print("Incoming forward facts:", incoming.num_rows)
```

### Filter by relation or knowledge category

```python
birth_facts = edges.filter(lambda row: row["relation"] == "BirthDate")
work_facts = edges.filter(lambda row: row["group"] == "Work")
```

### Use questions and statements for retrieval or QA research

```python
qa_edges = edges.filter(
    lambda row: row["question"] is not None and row["surface"] is not None
)

qa_view = qa_edges.select_columns(
    ["subject", "relation", "object", "question", "surface"]
)
```

Do not assume these rows are independent examples. Split by connected entities or graph components when entity leakage would invalidate an evaluation.

### Reconstruct the forward graph with NetworkX

```bash
python -m pip install datasets networkx
```

```python
import networkx as nx
from datasets import load_dataset

edges = load_dataset(
    "factprop/FACTPROP",
    "knowledge_graph",
    split="knowledge_edges",
)

graph = nx.DiGraph()
for row in edges:
    graph.add_edge(
        row["subject"],
        row["object_node_label"],
        relation=row["relation"],
        question=row["question"],
        surface=row["surface"],
        confidence=row["confidence"],
        group=row["group"],
    )

print(graph.number_of_nodes())  # 100015
print(graph.number_of_edges())  # 357205
print(graph.in_degree("Apple Inc."))  # 467
```

Using `object_node_label` reconstructs all 100,015 nodes and all 357,205 canonical forward edges. It does not restore the excluded inverse traversal edges, stored evidence, or checkpoint construction state. Use `factprop_graph_v1.pkl` when those are required.

### Explore a local multi-hop neighborhood

```python
entity = "Apple Inc."
undirected_view = graph.to_undirected(as_view=True)

distance = nx.single_source_shortest_path_length(
    undirected_view,
    entity,
    cutoff=2,
)

one_hop = [node for node, hops in distance.items() if hops == 1]
two_hop = [node for node, hops in distance.items() if hops == 2]
```

Directed and undirected distances answer different research questions. Record the chosen convention; do not assume this generic example reproduces a specific FACTPROP paper protocol.

### Analyze relation and connectivity distributions

```python
from collections import Counter

relation_counts = Counter(edges["relation"])
group_counts = Counter(value for value in edges["group"] if value is not None)
```

For the complete node-level degree distribution, load the unchanged entity index directly:

```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "factprop/FACTPROP",
    "data/entities.jsonl",
    repo_type="dataset",
)

all_nodes = load_dataset("json", data_files=path, split="train")
```

## Curated entity index

The optional `curated` split is intended for readable entity-level browsing:

```python
from datasets import load_dataset

entities = load_dataset(
    "factprop/FACTPROP",
    "entity_index",
    split="curated",
)

matches = entities.filter(lambda row: row["qid"] == "Q312")
print(matches[:])
```

Its fields are:

| Field | Meaning |
| --- | --- |
| `label` | Original graph-node label |
| `qid` | Wikidata QID |
| `forward_object_in_degree` | Forward facts pointing to this node |
| `qid_node_count` | Number of graph-node records sharing this QID |

The curated view requires a standard QID and a readable label. It excludes unresolved and numeric-only labels. It is therefore useful for browsing but must not be treated as the complete node set or as a quality-certified subset.

## Download the canonical connected graph

### Download everything with the Hugging Face CLI

```bash
python -m pip install huggingface_hub

hf download factprop/FACTPROP \
  --repo-type dataset \
  --local-dir FACTPROP-data
```

This downloads the complete release, including:

```text
FACTPROP-data/
├── factprop_graph_v1.pkl
├── LICENSE_DATA
├── THIRD_PARTY_NOTICES.md
├── data/
│   ├── knowledge_edges.parquet
│   ├── entities_curated.parquet
│   ├── entities_curated.jsonl
│   └── entities.jsonl
├── metadata/
├── GRAPH.md
├── load_graph.py
└── SHA256SUMS
```

### Download everything with Python

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="factprop/FACTPROP",
    repo_type="dataset",
    local_dir="FACTPROP-data",
)
```

### Download and verify only the graph checkpoint

```bash
python -m pip install networkx huggingface_hub
python load_graph.py
```

The included loader downloads `factprop_graph_v1.pkl`, verifies SHA-256, and returns `payload["graph"]`.

Python pickle loading can execute code. Load only the checkpoint from this trusted repository and verify:

```text
437a434260edbb019c85575d65b4775cb2461f145e17966ee3acc8e4625ce7c8
```

See [GRAPH.md](GRAPH.md) for the complete checkpoint schema.

## Canonical checkpoint contents

`factprop_graph_v1.pkl` contains a dictionary:

```text
payload
├── graph              # NetworkX DiGraph
├── state
├── seed_entities
├── validator_state
└── scheduler_stats
```

The graph has 100,015 nodes and 432,562 stored directed edges. Edge attributes include:

```text
relation, question, surface, evidence, confidence, group, is_inverse
```

Use `is_inverse is not True` when selecting original forward facts or reproducing FACTPROP popularity. Counting total NetworkX in-degree without this filter mixes forward facts with auxiliary traversal edges.

## BCE dates and numeric labels

The canonical graph stores BCE year literals as negative internal labels:

```text
Ancient Egypt --FoundingDate--> -3100
```

The edge `surface` expresses the readable meaning: approximately 3100 BCE. These values are date literals, not negative row IDs, negative connectivity, or corrupted entity indices.

To avoid two nearly identical target columns, `knowledge_edges` retains only the canonical `object_node_label`, such as `-3100`, and marks it as `object_type="date_literal"`. Use the `surface` field when a human-readable BCE expression is needed.

## Recommended uses

FACTPROP is suitable for:

- Knowledge updating and collateral-error propagation studies.
- Knowledge-graph topology and degree-distribution analysis.
- Entity connectivity and structural popularity research.
- Graph-aware retrieval and RAG experiments.
- Relation-conditioned factual QA dataset construction.
- Knowledge-editing neighborhood and multi-hop evaluation design.
- Robustness, forgetting, and rehearsal-strategy research.

The graph can serve as an empirical, real-world-derived factual network. It should not be described as a complete or statistically unbiased distribution of world knowledge.

## Evaluation and split guidance

This release intentionally does not impose train, validation, or test splits. Appropriate splitting depends on the task.

For predictive experiments:

- Avoid random edge splits when the same entities on both sides would create leakage.
- Consider entity-disjoint, relation-aware, temporal, or connected-component splits.
- Keep knowledge-update targets separate from ripple evaluation facts.
- Record whether auxiliary inverse edges are included.
- Record how literals, missing QIDs, and duplicate QIDs are handled.
- Pin the dataset revision and publish generated split IDs.

## Coverage, quality, and limitations

- The graph uses LLM-generated, Wikipedia-oriented factual candidates and Wikidata validation; it is not an exhaustive model of world knowledge.
- Construction choices—including seed entities, relation inventory, expansion policy, and validation coverage—shape the observed distribution.
- A stored edge should not be treated as permanently or universally correct. Facts can be time-sensitive, approximate, disputed, or affected by source limitations.
- `confidence` is a construction-time field, not a calibrated probability or human annotation score.
- 33,901 graph-node records have no QID mapping.
- 5,037 QIDs occur in more than one graph-node record.
- Missing QIDs do not imply missing nodes, low connectivity, or degree zero.
- Duplicate-QID nodes must be reviewed separately. Do not automatically sum their degrees or choose the maximum.
- Some expected mappings, including the Java and Python programming languages, are absent from the current mapping.
- The curated entity split is a display filter, not a gold quality label.
- Experiment inputs, model checkpoints, and complete paper outputs are not included.

These limitations are part of the released graph version. Mapping corrections should be maintained as separately reviewed records rather than silently replacing original nodes.

## Reproducibility checklist

For a reproducible use of FACTPROP, record:

- Hub revision or commit ID.
- Checksum of each downloaded artifact.
- Whether `knowledge_edges` or the canonical checkpoint was used.
- Whether inverse edges were included.
- Directed or undirected traversal convention.
- QID deduplication policy.
- Literal/date normalization policy.
- Entity, relation, and graph-component split policy.
- Any filtering or mapping corrections.

Run checksum verification from the downloaded repository:

```bash
shasum -a 256 -c SHA256SUMS
```

On Linux, use:

```bash
sha256sum -c SHA256SUMS
```

## File inventory

| File | Purpose |
| --- | --- |
| `factprop_graph_v1.pkl` | Canonical connected NetworkX graph |
| `LICENSE_DATA` | Dataset license notice and attribution requirements |
| `THIRD_PARTY_NOTICES.md` | Upstream provenance and source-specific terms |
| `data/knowledge_edges.parquet` | All forward knowledge connections for safe, efficient analysis |
| `data/entities_curated.parquet` | Viewer-ready mapped entity summary |
| `data/entities_curated.jsonl` | Equivalent JSONL curated summary |
| `data/entities.jsonl` | Complete original-order entity index |
| `browser-index.json` | Index used by the FACTPROP website |
| `GRAPH.md` | Canonical checkpoint schema |
| `load_graph.py` | Checksum-verifying checkpoint loader |
| `metadata/graph.json` | Graph counts, thresholds, export date, and source checksum |
| `metadata/release.json` | Release and mapping statistics |
| `SHA256SUMS` | Artifact integrity checksums |

## Provenance and versioning

The released construction code uses `gpt-4o-mini` to generate candidate triplets and natural-language fields, then validates supported relations through Wikidata API and SPARQL queries. Project materials describe these candidates as Wikipedia-derived; the released builder does not directly ingest Wikipedia article text. The entity popularity index was exported from the unchanged paper graph, and its source checksum is retained in `metadata/graph.json`.

`knowledge_edges` contains every forward edge from the checkpoint and uses the canonical target label in `object_node_label`. Topology and connectivity metrics are derived directly from the canonical graph. No graph nodes, mappings, edge topology, or checkpoint metadata were changed for this release.

Use a Hub revision to freeze data. Proposed mapping corrections or enhanced versions should use a new versioned artifact rather than overwrite `factprop_graph_v1.pkl`.

## License

FACTPROP is licensed under the
[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/),
subject to third-party rights and source-specific terms.

The license covers the FACTPROP authors' original graph compilation,
generated natural-language metadata, derived tables, documentation, and
applicable database rights. It does not relicense Wikidata content,
third-party materials, names, trademarks, personality rights, or facts that
are not protected by copyright.

When redistributing or adapting the dataset:

1. Credit the FACTPROP authors.
2. Cite the work below and link to this dataset repository.
3. Link to CC BY 4.0.
4. Indicate whether changes were made.

See [LICENSE_DATA](LICENSE_DATA) for the license notice and
[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) for Wikidata, Wikipedia,
and model-output provenance.

## Citation

Until a public paper identifier is available, cite the dataset as:

```bibtex
@dataset{zhang2026factprop,
  title     = {FACTPROP: A Connected Factual Knowledge Graph},
  author    = {Yuji Zhang and Weibing Wang and Cheng Qian and Duo Zhou and
               Dilek Hakkani-Tür and Kathleen McKeown and Chengxiang Zhai and
               Heng Ji},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/factprop/FACTPROP},
  note      = {Dataset for ``Popular Knowledge Propagates More Errors in LLM
               Knowledge Updating''}
}
```