# FactSynset Dataset

## Overview

FactSynset is the semantic equivalence layer of FactNet that aggregates similar FactStatements into unified semantic classes with normalized values. It provides a cross-lingual view of semantically equivalent facts, enabling reasoning across language barriers.

+ 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:

- `synset_id`: Unique identifier for the semantic equivalence class
- `aggregation_key`: Aggregation key (S||P||NormValue||NormQuals)
- `member_statement_ids`: List of FactStatement IDs in this synset
- `member_factsense_ids`: List of FactSense IDs associated with this synset
- `canonical_statement_id`: Representative FactStatement ID
- `canonical_mentions`: Best mentions per language (lang → {factsense_id, sentence, page_title, confidence})
- `subject_qid`: Subject entity QID
- `property_pid`: Property PID
- `normalized_value`: Normalized value representation
- `value_variants`: List of original value variants
- `qualifier_variants`: List of qualifier variants
- `aggregate_confidence`: Aggregated confidence score
- `source_count`: Number of independent references
- `language_coverage`: Language distribution (lang → mention_count)
- `time_span`: Temporal coverage information
- `aggregation_reason`: Reason for aggregation (e.g., value_normalization, qualifier_difference)
- `updated_at`: Last update timestamp

## Usage

FactSynsets provide a unified semantic view of facts across languages, enabling advanced applications like cross-lingual fact checking, multilingual knowledge graph completion, and semantic reasoning.

## 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}
}
```