I went to the Pubcon 2017 Conference this week in Las Vegas, Nevada, and gave a presentation about Semantic keywords and topic models based upon white papers and patents from Google. My focus was on things such as Context Vectors and Phrase-Based Indexing.
I promised on social media that I would post the presentation on my blog to answer questions if anyone had any.
I’ve been doing Semantic keywords and topic models research like this for years, where I’ve looked at other pages that rank well for keyword terms that I want to use, and identify phrases and terms that tend to appear upon those pages, and include them on pages that I am trying to optimize. So it made a lot of sense to start looking at semantic topic models research after reading about phrase-based indexing in 2005 and later.
Some of the terms I see when I search for Semantic keywords Research include such things as “improve your rankings,” and “conducting keyword research,” and “smarter content.” I see phrases that I’m not a fan of, such as “LSI Keywords,” which has as much scientific credibility as Keyword Density, which is next to none. There were researchers from Bell Labs, in 1990, who wrote a white paper and a patent about Latent Semantic Indexing, which was something that was used with small (less than 10,000 documents) and static collections of documents (the web is constantly changing and hasn’t been that small for a long time.)
Many people call themselves SEOs who tout LSI keywords as being keywords based upon having related meanings to other words; unfortunately, that has nothing to do with the LSI that was developed in 1990.
If you are going to present research or theories about LSI, it pays to do a little research first. Here’s my presentation. It includes links to patents and white papers that the ideas within it are based upon. I do look forward to your questions.
Keyword Research and Topic Modeling in a Semantic Web from Bill SlawskiLast Updated May 30, 2019