openbmb/factnet_factsynset

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 → {factsenseid, sentence, pagetitle, 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., valuenormalization, qualifierdifference)
• —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}
}