# Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources

arXiv: https://arxiv.org/abs/1502.03519 · HTML: https://mlsanity.com/paper/1502.03519/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2015.

## Built on by (most cited first, 21 of 21)

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- [A Survey on Data Quality Dimensions and Tools for Machine Learning](https://mlsanity.com/paper/2406.19614/) (2024)
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- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts](https://mlsanity.com/paper/2506.12552/) (2025)
- [Stability of Weighted Majority Voting under Estimated Weights](https://mlsanity.com/paper/2207.06118/) (2022)
- [Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similarity](https://mlsanity.com/paper/2311.04524/) (2023)
- [From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines](https://mlsanity.com/paper/2604.13468/) (2026)
- [REMOD: Relation Extraction for Modeling Online Discourse](https://mlsanity.com/paper/2102.11105/) (2021)
- [ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources](https://mlsanity.com/paper/2504.06271/) (2025)
- [Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations](https://mlsanity.com/paper/2509.08989/) (2025)
- [Training the Knowledge Base through Evidence Distillation and Write-Back Enrichment](https://mlsanity.com/paper/2603.25737/) (2026)
- [Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems](https://mlsanity.com/paper/2607.24117/) (2026)

## Abstract (arXiv)

The quality of web sources has been traditionally evaluated using exogenous signals such as the hyperlink structure of the graph. We propose a new approach that relies on endogenous signals, namely, the correctness of factual information provided by the source. A source that has few false facts is considered to be trustworthy. The facts are automatically extracted from each source by information extraction methods commonly used to construct knowledge bases. We propose a way to distinguish errors made in the extraction process from factual errors in the web source per se, by using joint inference in a novel multi-layer probabilistic model. We call the trustworthiness score we computed Knowledge-Based Trust (KBT). On synthetic data, we show that our method can reliably compute the true trustworthiness levels of the sources. We then apply it to a database of 2.8B facts extracted from the web, and thereby estimate the trustworthiness of 119M webpages. Manual evaluation of a subset of the results confirms the effectiveness of the method.