| # FactNet Relations Dataset | |
| ## Overview | |
| The Synset Relations dataset contains rich semantic relationships between FactSynsets, enabling advanced reasoning and cross-lingual fact retrieval. These relations capture hypernymy, causality, temporality, geographic relationships, and other semantic connections between facts. | |
| + 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: | |
| - `relation_id`: Unique identifier for the relation | |
| - `source_synset_id`: Source FactSynset ID | |
| - `target_synset_id`: Target FactSynset ID | |
| - `relation_type`: Type of relation (hypernym, causal, temporal, geographic, etc.) | |
| - `confidence`: Confidence score for the relation | |
| - `evidence_statement_ids`: FactStatements supporting this relation | |
| - `detection_method`: Method used to detect the relation | |
| - `metadata`: Additional relation-specific metadata | |
| ## Relation Types | |
| The dataset includes various relation types: | |
| - `equivalent`: Semantically equivalent facts | |
| - `hypernym`: Hierarchical relationships | |
| - `causal`: Cause-effect relationships | |
| - `geographic_location`/`geographic_contains`: Spatial relationships | |
| - `part_of`/`has_part`: Part-whole relationships | |
| - `member_of`: Membership relationships | |
| - `follows`/`followed_by`: Temporal sequence | |
| - `influenced_by`/`influences`: Influence relationships | |
| - And many others including `created_by`, `used_for`, `opposite_of`, etc. | |
| ## Usage | |
| Synset Relations enable advanced applications like: | |
| - Multi-hop reasoning across facts | |
| - Causal and temporal inference | |
| - Geographic and spatial reasoning | |
| - Semantic similarity computation | |
| - Hierarchical knowledge navigation | |
| ## 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} | |
| } | |
| ``` |