# 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.