| # FactSense Dataset | |
| ## Overview | |
| FactSense is the linguistic layer of FactNet that provides multilingual, natural language expressions of facts extracted from Wikipedia pages. Each FactSense instance represents a FactStatement realized in natural text with provenance information. | |
| + 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: | |
| - `factsense_id`: Unique identifier for the FactSense instance | |
| - `belongs_to_statement_id`: ID of the associated FactStatement | |
| - `language`: Language code (e.g., 'en', 'zh') | |
| - `page_id`: Wikipedia page ID | |
| - `page_title`: Title of the Wikipedia page | |
| - `page_namespace`: Wikipedia namespace | |
| - `match_type`: Type of match (sitelink, sitelink_pagelink, label_based, etc.) | |
| - `sentence`: Text snippet containing the fact mention (limited to 1000 chars) | |
| - `sentence_index`: Position of the sentence in the paragraph/page | |
| - `confidence`: Match confidence score (0.5-0.9) | |
| - `subject_qid`: Subject entity QID | |
| - `property_pid`: Property PID | |
| - `value_qid`: Value entity QID (if applicable) | |
| - `subject_label`: Label used for matching | |
| - `value_label`: Value label used for matching | |
| - `extraction_method`: Extraction strategy version | |
| - `extraction_ts`: Extraction timestamp | |
| ## Usage | |
| FactSense instances provide natural language evidence for facts across multiple languages, enabling applications in multilingual fact checking, knowledge-grounded generation, and cross-lingual information retrieval. | |
| ## License | |
| This dataset contains text derived from 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} | |
| } | |
| ``` |