In SEO, creating a dependable content is one of the biggest issues that every SEO services have to face. One of the ways that you can use is by using KBT “knowledge based Truth” in your content research. If you haven’t been familiar yet with KBT you can figure out what it is in (http://arxiv.org/pdf/1502.03519.pdf) to help you understand better the other 3 facts of Google’s truth algorithm.
Issue #1: Irrelevant Noise
The facts are identified by the algorithm through examining three factors which is known better as “Knowledge Triples,” consisting of a subject, a predicate, and an object. A subject is a “real-world entity” such as people, places or things. A predicate describes an attribute of that entity. According to the research paper, an object is “an entity, a string, a numerical value, or a date.” Those three attributes together from a fact, known in the research paper as Knowledge Triples and often referred to simply as Triples.
Issue #2: Duplicate Content
Since the KBT cannot sort out containing facts copied from other sites, then it is possible that content contained copying facts from “trusted” sources such as Wikipedia, Freebase, and other knowledge sources cannot be sorted out by KBT.
The researchers tried to apply scaled copy detection as part of knowledge-based trust algorithm but it’s simply not ready. This is a fourth issue that will delay the deployment of KBT to Google’s search results pages.
Issue 3: Accuracy
The accuracy of KBT is derived from a research which among the one hundred random high trust sites picked for review, 15 of the sites (15%) are errors. Two sites are topically irrelevant, twelve scored high because of trivial triples, and one website had both kinds of errors (topically irrelevant and a high number of trivial triples). In other words, in a random sample of high trust sites with low PageRank, KBT’s false positive percentage is revealed to be on the order of 15%.
Many research papers whose algorithms eventually make it into an algorithm usually demonstrate a vast improvement over previous efforts. That is not the case with Knowledge-Based Trust. While a Truth Algorithm makes an alarming headline, the truth is there are five important issues that need to be solved before it makes it to an algorithm near you.