It’s been a year since marketing agencies started hearing buzz about Knowledge-Based Trust, a Google research paper outlining a proposal to score web sites according to the accuracy of facts. It didn’t take long for Knowledge-Based Trust to earn the nickname the “Truth Algorithm” due to its method of assigning a “Trust Score” to filter sites with erroneous or misleading information.

While the title of this post may be a little misleading (“Trust Score” plummets), the paper at least hints that trustworthiness might help sites rise in Google’s rankings if the algo begins measuring quality score by facts, not just links and content.

Here’s the problem: The internet is famous for gossip, untruth and misdirection. Discount Viagra websites make the front page of Google, and gossip sites touting “news” stories are a dime a dozen.

Google currently measures incoming links to a web site as a factor for ranking. Sites with lots of backlinks (among other factors) are ranked higher. The problem with this method is that it can be gamed to make websites like those mentioned above rise in rankings.

Now Google is testing a method to measure the trustworthiness of a site, rather than (solely) its inbound links and content. The model measures sites based on the number of incorrect facts. The outcome is a “Knowledge-Based Trust score”.

Currently, links are a huge reputation signal implying authority. Knowledge-Based Trust may be a direction the algorithm is heading in, focusing more on veracity of content and less on link signals.

As part of its discovery, the team: “…evaluated each website according to the following 4 criteria”:

  • Triple correctness: whether at least 9 triples are correct.
  • Extraction correctness: whether at least 9 triples are correctly extracted (and hence we can evaluate the website according to what it really states).
  • Topic relevance: we decide the major topics for the website according to the website name and the introduction in the “About us” page; we then decide whether at least 9 triples are relevant to these topics (g., if the website is about business directories in South America but the extractions are about cities and countries in SA, we consider them as not topic relevant).
  • Non-trivialness: we decide whether the sampled triples state non-trivial facts (g., if most sampled triples from a Hindi movie website state that the language of the movie is Hindi, we consider it as trivial).

The research paper goes on to explain five reasons why KBT isn’t yet ready to go live. What will happen once the team solves the five outstanding problems? As a Portland marketing agency, we will watch and report back… But watch out TMZ.

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