Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources

Topics: E-E-A-T, Knowledge Graph, Ranking, Scoring, Xin Luna Dong

The document titled “Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources” discusses a novel approach to evaluating the reliability of web sources based on the accuracy of factual information they provide, rather than using traditional signals such as hyperlinks or browsing history.

The scientific paper Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources from Google deals with the algorithmic determination of the credibility of websites.

This scientific paper deals with how to determine the trustworthiness of online sources. In addition to the analysis of links, a new method is presented that is based on the verification of published information for accuracy.

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.

For this, methods of data mining are used, which I have already discussed in detail in the articles How can Google identify and interpret entities from unstructured content? and Natural language processing to build a semantic database.

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.

Using this approach, sources can be rated with a “trustworthiness score” without including the popularity factor. Websites that frequently provide incorrect information are devalued. Websites that publish information in line with the general consensus are rewarded. This also reduces the likelihood that websites that attract attention through fake news will gain visibility on Google.