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README.md

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+# FactNet Benchmarks
+
+This repository contains three benchmark datasets derived from FactNet:
+
+### 1. Knowledge Graph Completion (KGC)
+
+The KGC benchmark evaluates a model's ability to complete missing links in a knowledge graph.
+
+- **Format**: (subject, relation, object) triples
+- **Splits**: Train/Dev/Test
+- **Task**: Predict missing entity (either subject or object)
+- **Construction**: Extracted from entity-valued synsets and projected to (S, P, O) triples with careful cross-split collision handling
+
+### 2. Multilingual Knowledge Graph QA (MKQA)
+
+The MKQA benchmark evaluates knowledge graph question answering across multiple languages.
+
+- **Languages**: Multiple (en, zh, de, fr, etc.)
+- **Format**: Natural language questions with structured answers
+- **Task**: Answer factoid questions using knowledge graph information
+- **Construction**: Generated from FactSynsets with canonical mentions across languages
+
+### 3. Multilingual Fact Checking (MFC)
+
+The MFC benchmark evaluates fact verification capabilities across languages.
+
+- **Languages**: Multiple (en, zh, de, fr, etc.)
+- **Labels**: SUPPORTED, REFUTED, NOT_ENOUGH_INFO
+- **Format**: Claims with associated evidence units
+- **Construction**:
+  - SUPPORTED claims generated from synsets with FactSenses
+  - REFUTED claims generated by value replacement
+  - NOT_ENOUGH_INFO claims generated with no matching synsets
+  - Each claim associated with gold evidence units with character spans
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the KGC benchmark
+kgc_dataset = load_dataset("factnet/kgc_bench")
+
+# Load the MKQA benchmark for English
+mkqa_en_dataset = load_dataset("factnet/mkqa_bench", "en")
+
+# Load the MFC benchmark for English
+mfc_en_dataset = load_dataset("factnet/mfc_bench", "en")
+
+# Example of working with the MFC dataset
+for item in mfc_en_dataset["test"]:
+    claim = item["claim"]
+    label = item["label"]
+    evidence = item["evidence"]
+    print(f"Claim: {claim}")
+    print(f"Label: {label}")
+    print(f"Evidence: {evidence}")
+```
+
+## Construction Process
+
+FactNet and its benchmarks were constructed through a multi-phase pipeline:
+
+1. **Data Extraction**:
+   - Parsing Wikidata to extract FactStatements and labels
+   - Extracting Wikipedia pages using WikiExtractor
+   - Parsing pagelinks and redirects from SQL dumps
+
+2. **Elasticsearch Indexing**:
+   - Indexing Wikipedia pages, FactStatements, and entity labels
+   - Creating optimized indices for retrieval
+
+3. **FactNet Construction**:
+   - Building FactSense instances by linking statements to text
+   - Aggregating FactStatements into FactSynsets
+   - Building inter-synset relation edges
+
+4. **Benchmark Generation**:
+   - Constructing KGC, MKQA, and MFC benchmarks from the FactNet structure
+
+## Citation
+
+If you use FactNet benchmarks in your research, please cite:
+
+```
+@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}
+}
+```
+
+## Acknowledgements
+
+FactNet was built using Wikidata and Wikipedia data. We thank the communities behind these resources for their invaluable contributions to open knowledge.