[Podcast] The Art Of Semantic Search – Episode 01 Introduction

Podcast - The Art of Semantic Search

Hey! This is my introductory podcast into semantic seo and a bit of a background about me. The next episode will begin digging into all this technical jargon so you can implement technical seo using named entities in titles, pictures and body of content.

Tuesday, March 12th, 2019 Episode 02Where can I find out what is a known entity to Google and what is not?

Listen To The Art of Semantic Search On Spotify
Listen To The Art Of Semantic Search by Denver Prophit Jr. On Anchor.fm

Semantic SEO is the process of building more meaning into the words you use in your content. This means optimizing for the true intent of your users, not just answering a simple query.

Search Engine Journal - June 2017

Semantic SEO Terminologies

relating to meaning in language or logic.
the process of maximizing the number of visitors to a particular website by ensuring that the site appears high on the list of results returned by a search engine.
a thing with distinct and independent existence or being.
Image ALT
is a word or phrase that can be inserted as an attribute in an HTML document to tell Website viewers the nature or contents of an image. The alt text appears in a blank box that would normally contain the image.
H1 Tag
the HTML code tag indicating the main topic of your outline in grammatical syntax.

Why I should Focus On Entities Not Keywords


Bill Slawski - Director of SEO Research at Go Fish Digital

Google’s First Semantic Search Invention was Patented in 1999

This is officially part of the story I’m telling in a presentation I prepared for SMX East, in a couple of weeks in New York. The name of the session I’m in is “Hummingbird and the Entity Revolution,” which reminds me of a Prince song from the 1980s.

Patent Summary

Techniques for extracting information from a database are provided. A database such as the Web is searched for occurrences of tuples of information. The occurrences of the tuples of information that were found in the database are analyzed to identify a pattern in which the tuples of information were stored. Additional tuples of information can then be extracted from the database utilizing the pattern. This process can be repeated with the additional tuples of information, if desired.

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