| # 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} | |
| } | |
| ``` |