Create README.md

README.md

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+# FactNet 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.
+
+## 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}
+}
+```