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
