# FactNet Relations Dataset

## Overview

The Synset Relations dataset contains rich semantic relationships between FactSynsets, enabling advanced reasoning and cross-lingual fact retrieval. These relations capture hypernymy, causality, temporality, geographic relationships, and other semantic connections between facts.

+ Paper: https://arxiv.org/abs/2602.03417
+ Github: https://github.com/yl-shen/factnet
+ Dataset: https://huggingface.co/collections/openbmb/factnet

## Dataset Format

The dataset contains parquet files with the following key fields:

- `relation_id`: Unique identifier for the relation
- `source_synset_id`: Source FactSynset ID
- `target_synset_id`: Target FactSynset ID
- `relation_type`: Type of relation (hypernym, causal, temporal, geographic, etc.)
- `confidence`: Confidence score for the relation
- `evidence_statement_ids`: FactStatements supporting this relation
- `detection_method`: Method used to detect the relation
- `metadata`: Additional relation-specific metadata

## Relation Types

The dataset includes various relation types:
- `equivalent`: Semantically equivalent facts
- `hypernym`: Hierarchical relationships
- `causal`: Cause-effect relationships
- `geographic_location`/`geographic_contains`: Spatial relationships
- `part_of`/`has_part`: Part-whole relationships
- `member_of`: Membership relationships
- `follows`/`followed_by`: Temporal sequence
- `influenced_by`/`influences`: Influence relationships
- And many others including `created_by`, `used_for`, `opposite_of`, etc.

## Usage

Synset Relations enable advanced applications like:
- Multi-hop reasoning across facts
- Causal and temporal inference
- Geographic and spatial reasoning
- Semantic similarity computation
- Hierarchical knowledge navigation

## License

This dataset is derived from Wikidata and Wikipedia and is available under the CC BY-SA license.

## Citation

```
@article{shen2026factnet,
  title={FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding},
  author={Shen, Yingli and Lai, Wen and Zhou, Jie and Zhang, Xueren and Wang, Yudong and Luo, Kangyang and Wang, Shuo and Gao, Ge and Fraser, Alexander and Sun, Maosong},
  journal={arXiv preprint arXiv:2602.03417},
  year={2026}
}
```