| # FactNet Benchmarks |
|
|
| This repository contains three benchmark datasets derived from FactNet: |
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|
| ### 1. Knowledge Graph Completion (KGC) |
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| The KGC benchmark evaluates a model's ability to complete missing links in a knowledge graph. |
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| - **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 |
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| ### 2. Multilingual Knowledge Graph QA (MKQA) |
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| The MKQA benchmark evaluates knowledge graph question answering across multiple languages. |
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| - **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 |
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| ### 3. Multilingual Fact Checking (MFC) |
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| The MFC benchmark evaluates fact verification capabilities across languages. |
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| - **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 |
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|
| ## Usage |
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|
| ```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 |
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| FactNet and its benchmarks were constructed through a multi-phase pipeline: |
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| 1. **Data Extraction**: |
| - Parsing Wikidata to extract FactStatements and labels |
| - Extracting Wikipedia pages using WikiExtractor |
| - Parsing pagelinks and redirects from SQL dumps |
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| 2. **Elasticsearch Indexing**: |
| - Indexing Wikipedia pages, FactStatements, and entity labels |
| - Creating optimized indices for retrieval |
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| 3. **FactNet Construction**: |
| - Building FactSense instances by linking statements to text |
| - Aggregating FactStatements into FactSynsets |
| - Building inter-synset relation edges |
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| 4. **Benchmark Generation**: |
| - Constructing KGC, MKQA, and MFC benchmarks from the FactNet structure |
|
|
| ## Citation |
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| If you use FactNet benchmarks in your research, please cite: |
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|
| ``` |
| @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} |
| } |
| ``` |
|
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| ## Acknowledgements |
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| FactNet was built using Wikidata and Wikipedia data. We thank the communities behind these resources for their invaluable contributions to open knowledge. |
