Google leverages entity graphs similarly for search
Google leverages relational data (in a very similarly way to the above example) to form better understandings of digital objects to help provide the most relevant search results.
A kind of scary example of this is Google’s Expander: A large-scale ML platform to “exploit relationships between data objects.”
Machine learning is typically “supervised” (training data is provided, which is more common) or “unsupervised” (no training data). Expander, however, is “semi-supervised,” meaning that it’s bridging the gap between provided and not-provided data. ← SEO pun intended!
Expander leverages a large, graph-based system to infer relationships between datasets. Ever wonder why you start getting ads about a product you started emailing your friend about?
Expander is bridging the gap between platforms to better understand online data and is only going to get better.
People Also Ask - How to use PAA to rank high on Google
Part 2 .
1. Relational entity graphs for search
Here is a slide from a Google I/O 2016 talk that showcases a relational word graph for search results:
Solid edges represent stronger relationships between nodes than the dotted lines. The above example shows there is a strong relationship between “What are the traditions of halloween” and “halloween tradition,” which makes sense. People searching for either of those would each be satisfied by quality content about “halloween traditions.”
Edge strength can also be determined by distributional similarity, lexical similarity, similarity based on word embeddings, etc.
2. Infinite PAA machine learning hypothesis:
Google is providing additional PAAs based on the strongest relational edges to the expanded query.
You can continue to see this occur in infinite PAAs datasets. When a word with two lexical similarities overlaps the suggested PAAs, the topic changes because of it:
The above topic change occurred through a series of small relational suggestions. A PAA above this screenshot was “What is SMO stands for?” (not a typo, just a neural network doing its best people!) which led to "What is the meaning of SMO?", to “What is a smo brace?” (for ankles).
This immediately made me think of the relational word graph and what I envision Google is doing:
My hypothesis is that the machine learning model computes that because I’m interested in “SMO,” I might also be interested in ankle brace “SMO.”
There are ways for SEOs and digital marketers to leverage topical relevance and capture PAAs opportunities.
3. 4 ways to optimize for machine learning & expand your topical reach for PAAs:
Topical connections can always be made within your content, and by adding additional high quality topically related content, you can strengthen your content’s edges (and expand your SERP real estate). Here are some quick and easy ways to discover related topics:
#1: Quickly discover Related Topics via MozBar
MozBar is a free SEO browser add-on that allows you to do quick SEO analysis of web pages and SERPs. The On-Page Content Suggestions feature is a quick and simple way to find other topics related to your page.
Step 1: Activate MozBar on the page you are trying to expand your keyword reach with, and click the Page Optimization:
Step 2: Enter in the word you are trying to expand your keyword reach with:
Step 3: Click On-Page Content Suggestions for your full list of related keyword topics.
Step 4: Evaluate which related keywords can be incorporated naturally into your current on-page content. In this case, it would be beneficial to incorporate “seo tutorial,” “seo tools,” and “seo strategy” into the Beginner’s Guide to SEO.
Step 5: Some may seem like an awkward add to the page, like “seo services” and “search engine ranking,” but are relevant to the products/services that you offer. Try adding these topics to a better-fit page, creating a new page, or putting together a strong FAQ with other topically related questions.
#2: Wikipedia page + SEOBook Keyword Density Checker*
Let’s say you're trying to expand your topical keywords in an industry you’re not very familiar with, like "roof repair." You can use this free hack to pull in frequent and related topics.
Step 1: Find and copy the roof Wikipedia page URL.
Step 2: Paste the URL into SEOBook’s Keyword Density Checker:
Step 3: Hit submit and view the most commonly used words on the Wikipedia page:
Step 4: You can dive even deeper (and often more topically related) by clicking on the "Links" tab to evaluate the anchor text of on-page Wikipedia links. If a subtopic is important enough, it will likely have another page to link to:
Step 5: Use any appropriate keyword discoveries to create stronger topic-based content ideas.
*This tactic was mentioned in Experts On The Wire episode on keyword research tools.
