FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding

• URL: http://arxiv.org/abs/2602.03417v1
• Date: Tue, 03 Feb 2026 11:44:11 GMT
• Title: FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
• Authors: Yingli Shen, Wen Lai, Jie Zhou, Xueren Zhang, Yudong Wang, Kangyang Luo, Shuo Wang, Ge Gao, Alexander Fraser, Maosong Sun,
• Abstract summary: LLMs exhibit remarkable fluency, their utility is often compromised by factual hallucinations and a lack of traceable provenance.<n>We introduce FactNet, a massive, open-source resource designed to unify 1.7 billion atomic assertions with 3.01 billion auditable evidence pointers derived exclusively from 316 Wikipedia editions.
• Score: 81.2130536158575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While LLMs exhibit remarkable fluency, their utility is often compromised by factual hallucinations and a lack of traceable provenance. Existing resources for grounding mitigate this but typically enforce a dichotomy: they offer either structured knowledge without textual context (e.g., knowledge bases) or grounded text with limited scale and linguistic coverage. To bridge this gap, we introduce FactNet, a massive, open-source resource designed to unify 1.7 billion atomic assertions with 3.01 billion auditable evidence pointers derived exclusively from 316 Wikipedia editions. Unlike recent synthetic approaches, FactNet employs a strictly deterministic construction pipeline, ensuring that every evidence unit is recoverable with byte-level precision. Extensive auditing confirms a high grounding precision of 92.1%, even in long-tail languages. Furthermore, we establish FactNet-Bench, a comprehensive evaluation suite for Knowledge Graph Completion, Question Answering, and Fact Checking. FactNet provides the community with a foundational, reproducible resource for training and evaluating trustworthy, verifiable multilingual systems.

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