SEO

Query Relaxation And Scoping As Part Of Semantic Search

Published

2 years ago

June 10, 2022

Query Relaxation And Scoping As Part Of Semantic Search

The right search query is a Goldilocks-style effort: Not too specific that you get no results, and not too broad that you get too many.

Semantic search, meanwhile, is all about understanding what searchers throw into a search box.

In other words, with semantic search, we meet searchers where they are instead of requiring them to meet us where we are.

Enter query relaxation and query scoping.

Search engines get searchers to the right content right away through techniques like synonyms, query word removal, and query scoping.

We avoid missing out on relevant information that wouldn’t otherwise appear, and we leave out information that isn’t relevant.

Query relaxation and scoping are tied very closely with the concept of precision and recall.

Precision measures whether the returned results are relevant, and recall is whether relevant results are returned.

One way to increase recall specifically is through query expansion.

Query Expansion

Query expansion is all about expanding what the query will match with the hope of having better results.

The main reason a search engine might apply query expansion is due to some indication that the “base” search results without query expansion would not be satisfactory for the searcher.

In this series, we have already seen some ways to expand queries.

Typo tolerance, plural ignoring, and stemming and lemmatization are all ways to increase the recall of searches.

We’ve already seen those query expansion methods among the bedrocks of search, but other query expansion methods are also just as fundamental.

An article in Search Engine Journal from 2008 covers how Google performs query expansion!

The article discusses not just stemming and typo tolerance but also translations, word removals, and synonyms.

Synonyms And Alternatives

There’s a reason George Orwell introduced Newspeak in his novel 1984 and why it resonated in a story about life utterly controlled to the point of blandness.

Linguistic richness is driven by the ability to say the same thing, or nearly the same thing, with different words and phrases. “Great” can be “awesome,” and “low-cost” is a near neighbor to “cheap.”

Meanwhile, these different words can help us more precisely refer to items similar in all but the smallest ways.

These differences are sometimes so small that this precision instead breeds confusion and less likely to find what we want.

A customer wanting a rocking chair may not know whether to search for “rockers,” “rocking chairs,” or simply “chairs.”

This is where synonyms and alternatives provide value.

They help us expand recall in search results.

Synonyms and alternatives are similar, but they are not the same.

(You could say that they are not synonyms.)

Synonyms refer to two words or phrases that mean the same thing.

Alternatives instead refer to similar words or phrases but have some degrees of difference.

Synonyms

Often, synonyms make their way into a search engine through synonym lists.

These lists can come from predefined lists, such as general ecommerce terms.

The problem with predefined lists is that synonyms for one company’s search engine won’t necessarily work for another.

Quick: What’s a console? You may immediately think of video games, but someone else might think of a car or music.

For that reason, many synonym lists are created in-house.

At the beginning of a search implementation process, internal subject matter experts think of all of the words that could be synonyms for other words and add them to the search engine configuration.

(This, in reality, is often an idealized view of what happens. Often the person creating the synonym list is not a subject matter expert, but instead, the person implementing the search engine.)

Generally, this initial list will provide a good starting point, but there are sure to be missing synonyms.

The only real way to discover which terms your searchers will use is to let them search.

Using Analytics To Discover Synonyms

You’ll see very quickly in your analytics queries that could use new synonyms.

These queries are returning zero results and are a sign that searchers are looking for something they can’t find.

Now, not all of these queries will give you a new synonym.

Sometimes, searchers are looking for items that you just don’t have.

Nonetheless, you’ll see queries where you think immediately, “oh, we have that one,” and “I didn’t know people asked for it like that.”

There will also be times when a query returns results but not what the searcher wants.

These queries can also give you ideas for synonyms if you track “search refinements.”

Search refinements represent when searchers search and then search again.

This implies that the searchers didn’t find what they wanted the first time and tried again to find something better.

Someone searching for “Dell laptop” and following it up with “Dell notebook” is saying that “laptop” and “notebook” are related, but the search results for “laptop” were insufficient.

While there’s nothing wrong with looking for those trends in your analytics manually (it can be a good activity to slowly ease into the work week), you’ll be a lot more productive if you have a system that proactively sources them for you.

Some systems may even apply synonyms on your behalf, but this isn’t always helpful.

A human can spot refinements that don’t show valid synonyms or may see that the system is suggesting an incorrect type of synonym.

Types Of Synonyms

That’s right: There are different types of synonyms.

This concept may seem strange at first, but it’s probably not far from how most people think of them.

“Two-way” is the first type of synonym. These synonyms are direct replacements for each other.

“Small” and “mini” are two-way synonyms of each other.

The words don’t need to be perfect replacements but can be close enough that people might use one for the other.

For example, “rope” and “string” don’t describe the same thing, but they are close enough to be worthy two-way synonyms.

It can be useful to think of the query created through the use of synonyms.

If we take a query of “small cheese pizza” and expand that out, you can think of the query now as “(small or mini) and cheese and pizza.”

“One-way” is the next type of synonym.

This type is often used for words that refer to an object that belongs to a larger category.

“PlayStation” is a type of video game “console,” but a “console” is not a type of “PlayStation.”

If you add a one-way synonym to the search configuration, you can have PlayStations show up whenever someone searches for “console.”

Why not a two-way synonym between these two terms?

Because two-way synonyms are transitive.

If term one and term two are two-way synonyms, and terms two and three are two-way synonyms, then terms one and three are two-way.

In a more direct example, “PlayStation” and “console” and “Xbox” and “console” as two groups of two-way synonyms would mean that “PlayStation” and “Xbox” are synonyms, and searchers would see Playstations when searching for Xboxes, and vice versa.

“Alternative corrections” is the final type.

These are used when the words aren’t precise replacements for each other, and you want the exact match to appear higher than the alternative.

For example, you might say that “pants” are an alternative to “shorts,” but when someone searches the word “shorts,” then all shorts should appear higher than pants generally.

All synonym types, by their nature, expand recall.

However, the hit on precision should be minimal because these synonyms are “pointers” to similar concepts.

You would expect a better search experience for the end user.

Query Word Removal

Sometimes searchers will use a query that doesn’t return anything because the query was too specific or used a word that didn’t exist in any of the records.

Remove one word, or two words, from the query, and perfectly decent results would come back.

This is a great time to use query word removal.

Stop Words

Perhaps the most common query word removal step is removing “stop words.”

Stop words are very common words that provide meaning for communication but don’t help with retrieval. Words such as “the” or “an” can remove otherwise good matches.

This is more common in queries oriented toward natural language, such as voice search queries.

An example of this would be searching for “an orange shirt” on a product search engine.

If the search engine searches over the title, color, and category, there might be plenty of records that have “shirt” as a category and “orange” as a color, but none that include the word “an.”

Now, really, does the word “an” provide any useful information here?

No, it doesn’t, and the search engine can safely remove it without losing precision.

Unlike synonyms, you generally do not want to create your own stop word lists, and most search engines have them built-in per language.

However, there are times when you will want to expand on the built-in list, such as if you have an industry term that is so common that it doesn’t provide any value to a query.

Removing Words If No Results

Then there are queries where all of the words bring value but searched together, bring back no results.

Often searchers will be happy with less precise results in exchange for increased recall. In these situations, we want to remove words to put results in front of the user.

There are two main ways to do this: make all query words optional or remove words from the query.

If you make all of the query words optional when there are no results, you assume that records that match more words are more relevant, all else being equal.

An alternative is to remove query words one-by-one until you find matching records or there are no more words left in the query.

You can start by removing the first words or the last words. Last word removal tends to be more common.

Making all of the query words optional and then sorting by the number of matching words is generally the better approach, especially when paired with the removal of stop words.

This is, however, a less ideal approach when precision is important, and you want to show that, indeed, there were no results that matched all of the query words.

One person may be alright with seeing Uniqlo v-neck sweaters for a query of “Gucci v-neck sweaters,” while another sees those results as completely irrelevant.

Of course, another scenario is to know which words are actually providing the most value to the query and mark them as optional.

This is generally not seen in keyword-based search engines, but there have been some search engines that will take a similar approach for stop words.

For example, some search engines have experimented with discounting common words automatically without stop word lists, using inverse document frequency.

As with synonyms, query word removal will expand recall, usually without a hit on precision. Because stop words don’t provide much value to the result, you won’t lose out on good results by not including them.

Similarly, removing words when there are no results has no precision to lessen because there are no results that could be precise.

Query Scoping

We’ve primarily looked at situations where a searcher is overly precise and the search engine needs to expand the query to improve recall.

There are, likewise, times when the search engine can understand the user intent, and query scoping can increase precision.

Search expert Daniel Tunkelang calls query scoping “one of the most effective ways to capture query intent.”

He identifies two major steps in query scoping. The first is query tagging, followed by the scoping itself.

Query tagging identifies the parts of a query with the attributes they likely belong to.

For example, “Marcia” will most likely match to a “name” attribute, while “The Brady Bunch” maps to a “show title” attribute.

Query scoping takes this mapping and restricts attribute searching for these query parts.

The search engine doesn’t search “Brady” inside of the “name” attribute or “Marcia” in the “show title” attribute.

This kind of query scoping reduces recall, as we won’t see results that have that text in other attributes.

However, the outcome should be that we have higher precision because we aren’t searching for irrelevant attributes.

We could increase precision even further by filtering results by known attribute values.

This doesn’t even require machine learning, as the search engine can do a simple match between facet values and text in a query.

This reduces recall heavily, so we can also find a nice balance where we instead boost results with matching values rather than filtering.

The boosted results will tend to be the best matching ones because the query-filter match gives you a signal that it is what the searcher wants.

Through your analytics or hands-on experience, if you find that your search is missing user intent and requiring searches to be “just right,” then query expansion and query scoping are two ways to calibrate your precision and recall.

These approaches will let in results that should be there and leave out the ones that shouldn’t.

More resources:

Featured Image: penguiin/Shutterstock

Related Topics:Part Query Relaxation Scoping Search Semantic

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SEO

Big Update To Google’s Ranking Drop Documentation

Published

14 hours ago

April 27, 2024

Max

Google updates documentation for diagnosing ranking drops

Google updated their guidance with five changes on how to debug ranking drops. The new version contains over 400 more words that address small and large ranking drops. There’s room to quibble about some of the changes but overall the revised version is a step up from what it replaced.

Change# 1: Downplays Fixing Traffic Drops

The opening sentence was changed so that it offers less hope for bouncing back from an algorithmic traffic drop. Google also joined two sentences into one sentence in the revised version of the documentation.

The documentation previously said that most traffic drops can be reversed and that identifying the reasons for a drop aren’t straightforward. The part about most of them can be reversed was completely removed.

Here is the original two sentences:

“A drop in organic Search traffic can happen for several reasons, and most of them can be reversed. It may not be straightforward to understand what exactly happened to your site”

Now there’s no hope offered for “most of them can be reversed” and more emphasis on understanding what happened is not straightforward.

This is the new guidance

“A drop in organic Search traffic can happen for several reasons, and it may not be straightforward to understand what exactly happened to your site.”

Change #2 Security Or Spam Issues

Google updated the traffic graph illustrations so that they precisely align with the causes for each kind of traffic decline.

The previous version of the graph was labeled:

“Site-level technical issue (Manual Action, strong algorithmic changes)”

The problem with the previous label is that manual actions and strong algorithmic changes are not technical issues and the new version fixes that issue.

The updated version now reads:

“Large drop from an algorithmic update, site-wide security or spam issue”

Change #3 Technical Issues

There’s one more change to a graph label, also to make it more accurate.

This is how the previous graph was labeled:

“Page-level technical issue (algorithmic changes, market disruption)”

The updated graph is now labeled:

“Technical issue across your site, changing interests”

Now the graph and label are more specific as a sitewide change and “changing interests” is more general and covers a wider range of changes than market disruption. Changing interests includes market disruption (where a new product makes a previous one obsolete or less desirable) but it also includes products that go out of style or loses their trendiness.

Graph titled

Change #4 Google Adds New Guidance For Algorithmic Changes

The biggest change by far is their brand new section for algorithmic changes which replaces two smaller sections, one about policy violations and manual actions and a second one about algorithm changes.

The old version of this one section had 108 words. The updated version contains 443 words.

A section that’s particularly helpful is where the guidance splits algorithmic update damage into two categories.

Two New Categories:

Small drop in position? For example, dropping from position 2 to 4.
Large drop in position? For example, dropping from position 4 to 29.

The two new categories are perfect and align with what I’ve seen in the search results for sites that have lost rankings. The reasons for dropping up and down within the top ten are different from the reasons why a site drops completely out of the top ten.

I don’t agree with the guidance for large drops. They recommend reviewing your site for large drops, which is good advice for some sites that have lost rankings. But in other cases there’s nothing wrong with the site and this is where less experienced SEOs tend to be unable to fix the problems because there’s nothing wrong with the site. Recommendations for improving EEAT, adding author bios or filing link disavows do not solve what’s going on because there’s nothing wrong with the site. The problem is something else in some of the cases.

Here is the new guidance for debugging search position drops:

“Algorithmic update
Google is always improving how it assesses content and updating its search ranking and serving algorithms accordingly; core updates and other smaller updates may change how some pages perform in Google Search results. We post about notable improvements to our systems on our list of ranking updates page; check it to see if there’s anything that’s applicable to your site.
If you suspect a drop in traffic is due to an algorithmic update, it’s important to understand that there might not be anything fundamentally wrong with your content. To determine whether you need to make a change, review your top pages in Search Console and assess how they were ranking:
Small drop in position? For example, dropping from position 2 to 4.
Large drop in position? For example, dropping from position 4 to 29.
Keep in mind that positions aren’t static or fixed in place. Google’s search results are dynamic in nature because the open web itself is constantly changing with new and updated content. This constant change can cause both gains and drops in organic Search traffic.
Small drop in position
A small drop in position is when there’s a small shift in position in the top results (for example, dropping from position 2 to 4 for a search query). In Search Console, you might see a noticeable drop in traffic without a big change in impressions.
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Small fluctuations in position can happen at any time (including moving back up in position, without you needing to do anything). In fact, we recommend avoiding making radical changes if your page is already performing well.
Large drop in position
A large drop in position is when you see a notable drop out of the top results for a wide range of terms (for example, dropping from the top 10 results to position 29).
In cases like this, self-assess your whole website overall (not just individual pages) to make sure it’s helpful, reliable and people-first. If you’ve made changes to your site, it may take time to see an effect: some changes can take effect in a few days, while others could take several months. For example, it may take months before our systems determine that a site is now producing helpful content in the long term. In general, you’ll likely want to wait a few weeks to analyze your site in Search Console again to see if your efforts had a beneficial effect on ranking position.
Keep in mind that there’s no guarantee that changes you make to your website will result in noticeable impact in search results. If there’s more deserving content, it will continue to rank well with our systems.”

Change #5 Trivial Changes

The rest of the changes are relatively trivial but nonetheless makes the documentation more precise.

For example, one of the headings was changed from this:

You recently moved your site

To this new heading:

Site moves and migrations

Google’s Updated Ranking Drops Documentation

Google’s updated documentation is a well thought out but I think that the recommendations for large algorithmic drops are helpful for some cases and not helpful for other cases. I have 25 years of SEO experience and have experienced every single Google algorithm update. There are certain updates where the problem is not solved by trying to fix things and Google’s guidance used to be that sometimes there’s nothing to fix. The documentation is better but in my opinion it can be improved even further.

Read the new documentation here:

Debugging drops in Google Search traffic

Review the previous documentation:

Internet Archive Wayback Machine: Debugging drops in Google Search traffic

Featured Image by Shutterstock/Tomacco

SEO

Google March 2024 Core Update Officially Completed A Week Ago

Published

1 day ago

April 27, 2024

Max

Google has officially completed its March 2024 Core Update, ending over a month of ranking volatility across the web.

However, Google didn’t confirm the rollout’s conclusion on its data anomaly page until April 26—a whole week after the update was completed on April 19.

Many in the SEO community had been speculating for days about whether the turbulent update had wrapped up.

The delayed transparency exemplifies Google’s communication issues with publishers and the need for clarity during core updates

Google March 2024 Core Update Timeline & Status

First announced on March 5, the core algorithm update is complete as of April 19. It took 45 days to complete.

Unlike more routine core refreshes, Google warned this one was more complex.

Google’s documentation reads:

“As this is a complex update, the rollout may take up to a month. It’s likely there will be more fluctuations in rankings than with a regular core update, as different systems get fully updated and reinforce each other.”

The aftershocks were tangible, with some websites reporting losses of over 60% of their organic search traffic, according to data from industry observers.

The ripple effects also led to the deindexing of hundreds of sites that were allegedly violating Google’s guidelines.

Addressing Manipulation Attempts

In its official guidance, Google highlighted the criteria it looks for when targeting link spam and manipulation attempts:

Creating “low-value content” purely to garner manipulative links and inflate rankings.
Links intended to boost sites’ rankings artificially, including manipulative outgoing links.
The “repurposing” of expired domains with radically different content to game search visibility.

The updated guidelines warn:

“Any links that are intended to manipulate rankings in Google Search results may be considered link spam. This includes any behavior that manipulates links to your site or outgoing links from your site.”

John Mueller, a Search Advocate at Google, responded to the turbulence by advising publishers not to make rash changes while the core update was ongoing.

However, he suggested sites could proactively fix issues like unnatural paid links.

Mueller stated on Reddit:

“If you have noticed things that are worth improving on your site, I’d go ahead and get things done. The idea is not to make changes just for search engines, right? Your users will be happy if you can make things better even if search engines haven’t updated their view of your site yet.”

Emphasizing Quality Over Links

The core update made notable changes to how Google ranks websites.

Most significantly, Google reduced the importance of links in determining a website’s ranking.

In contrast to the description of links as “an important factor in determining relevancy,” Google’s updated spam policies stripped away the “important” designation, simply calling links “a factor.”

This change aligns with Google’s Gary Illyes’ statements that links aren’t among the top three most influential ranking signals.

Instead, Google is giving more weight to quality, credibility, and substantive content.

Consequently, long-running campaigns favoring low-quality link acquisition and keyword optimizations have been demoted.

With the update complete, SEOs and publishers are left to audit their strategies and websites to ensure alignment with Google’s new perspective on ranking.

Core Update Feedback

Google has opened a ranking feedback form related to this core update.

You can use this form until May 31 to provide feedback to Google’s Search team about any issues noticed after the core update.

While the feedback provided won’t be used to make changes for specific queries or websites, Google says it may help inform general improvements to its search ranking systems for future updates.

Google also updated its help documentation on “Debugging drops in Google Search traffic” to help people understand ranking changes after a core update.

Featured Image: Rohit-Tripathi/Shutterstock

FAQ

After the update, what steps should websites take to align with Google’s new ranking criteria?

After Google’s March 2024 Core Update, websites should:

Improve the quality, trustworthiness, and depth of their website content.
Stop heavily focusing on getting as many links as possible and prioritize relevant, high-quality links instead.
Fix any shady or spam-like SEO tactics on their sites.
Carefully review their SEO strategies to ensure they follow Google’s new guidelines.

SEO

Google Declares It The “Gemini Era” As Revenue Grows 15%

Published

2 days ago

April 26, 2024

Max

Alphabet Inc., Google’s parent company, announced its first quarter 2024 financial results today.

While Google reported double-digit growth in key revenue areas, the focus was on its AI developments, dubbed the “Gemini era” by CEO Sundar Pichai.

The Numbers: 15% Revenue Growth, Operating Margins Expand

Alphabet reported Q1 revenues of $80.5 billion, a 15% increase year-over-year, exceeding Wall Street’s projections.

Net income was $23.7 billion, with diluted earnings per share of $1.89. Operating margins expanded to 32%, up from 25% in the prior year.

Ruth Porat, Alphabet’s President and CFO, stated:

“Our strong financial results reflect revenue strength across the company and ongoing efforts to durably reengineer our cost base.”

Google’s core advertising units, such as Search and YouTube, drove growth. Google advertising revenues hit $61.7 billion for the quarter.

The Cloud division also maintained momentum, with revenues of $9.6 billion, up 28% year-over-year.

Pichai highlighted that YouTube and Cloud are expected to exit 2024 at a combined $100 billion annual revenue run rate.

Generative AI Integration in Search

Google experimented with AI-powered features in Search Labs before recently introducing AI overviews into the main search results page.

Regarding the gradual rollout, Pichai states:

“We are being measured in how we do this, focusing on areas where gen AI can improve the Search experience, while also prioritizing traffic to websites and merchants.”

Pichai reports that Google’s generative AI features have answered over a billion queries already:

“We’ve already served billions of queries with our generative AI features. It’s enabling people to access new information, to ask questions in new ways, and to ask more complex questions.”

Google reports increased Search usage and user satisfaction among those interacting with the new AI overview results.

The company also highlighted its “Circle to Search” feature on Android, which allows users to circle objects on their screen or in videos to get instant AI-powered answers via Google Lens.

Reorganizing For The “Gemini Era”

As part of the AI roadmap, Alphabet is consolidating all teams building AI models under the Google DeepMind umbrella.

Pichai revealed that, through hardware and software improvements, the company has reduced machine costs associated with its generative AI search results by 80% over the past year.

He states:

“Our data centers are some of the most high-performing, secure, reliable and efficient in the world. We’ve developed new AI models and algorithms that are more than one hundred times more efficient than they were 18 months ago.

How Will Google Make Money With AI?

Alphabet sees opportunities to monetize AI through its advertising products, Cloud offerings, and subscription services.

Google is integrating Gemini into ad products like Performance Max. The company’s Cloud division is bringing “the best of Google AI” to enterprise customers worldwide.

Google One, the company’s subscription service, surpassed 100 million paid subscribers in Q1 and introduced a new premium plan featuring advanced generative AI capabilities powered by Gemini models.

Future Outlook

Pichai outlined six key advantages positioning Alphabet to lead the “next wave of AI innovation”:

Research leadership in AI breakthroughs like the multimodal Gemini model
Robust AI infrastructure and custom TPU chips
Integrating generative AI into Search to enhance the user experience
A global product footprint reaching billions
Streamlined teams and improved execution velocity
Multiple revenue streams to monetize AI through advertising and cloud

With upcoming events like Google I/O and Google Marketing Live, the company is expected to share further updates on its AI initiatives and product roadmap.

Featured Image: Sergei Elagin/Shutterstock