README.md

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}
}