Please visit Search Engine Land for the full article.
Please visit Search Engine Land for the full article.
The combination of semantics (the science of meaning in language) with search engines that process billions of queries seems a very natural one.
Semantic search has been effective, too; by understanding the intent of a query and the context of the user, the accuracy of results on search engines like Google and Bing has increased significantly.
Search engine results pages today look markedly different to their earlier iterations and, with improvements in local search, voice recognition, and machine learning, they will continue to change over the next few years too.
There is a lot of fascinating theory behind all of this, but we can sometimes focus on this to the detriment of our work today.
Significant algorithm updates like Hummingbird, or the more recent launch of RankBrain, have a big impact on users. As marketers, we need to know exactly what this means for our strategy, our expectations, and our campaign measurement.
As such, this article will focus on some real-world examples of semantic search and provide a practical framework to help marketers avail of the opportunities it brings.
Semantic search in action
Let’s start with a simple example to shed light on how semantic search works. We’ll use a common, everyday search query like [will smith]. This screenshot is what I see above the fold on desktop:
When Google processes this query, it recognizes instantaneously that I am searching for the actor and all-round entertainer Will Smith, but also that the intent of my search is unclear. Therefore, it serves a varied array of options for me to click on. I may want to read news about the Fresh Prince, I may want to see his filmography, I may want to see if he has any new albums in the pipeline. Perhaps I want to see all three.
As is highlighted on the right-hand side in the knowledge panel, Google can retrieve all of this information from its index of 808,000,000 Will Smith-related results, but also from its own vast database of information about noteworthy people and institutions.
I can help Google out here by refining my search. Next, I ask [who is he married to]:
As we can see, results are pulled to the top of the results to highlight his current and former spouse.
This is a demonstration of conversational search in action.
Just like a person would in a conversation, Google knows the ‘he’ in my question refers to Will Smith. I don’t need to state this again. Google also needs to know what the connection is between ‘he’ and both Jada Pinkett Smith and Sheree Zampino.
These may seem like minor changes, but they hint at a fundamental shift in how Google works. Factor in voice search and it is easy to see how important this conversational element is.
If we extend this out to ask about Will Smith’s music, we can start to conceptualize just how complex Google’s network of interconnected entities is:
Asking what an artist’s best song is strays into the realm of subjectivity, so Google pulls the track listing from Will Smith’s greatest hits. Or at least, I hope that’s what’s happening here. If Google genuinely thinks ‘Girls Ain’t Nothing But Trouble’ is Will Smith’s best song, I’ll lose faith in them.
In terms of natural language processing, however, this search query is now quite convoluted. In this last instance, Google has had to keep track of who we’re asking about, having deviated once already to ask who his spouse is; then pull an indirect, best-fit answer to my question about Will Smith’s best song.
Let’s try one more, then we’ll give Google a break:
You get the idea.
We’ve come an awfully long way from the exact keyword matching of just a few years ago.
Furthermore, all of this serves an important illustrative purpose and it’s one that matters for anyone that wants to rank via SEO in 2017.
Why does it matter for brands?
The technology that underpins the above answers is utilized for all queries, so it is very significant for brands. Just launching a page on a website and ‘optimizing it for SEO’ clearly isn’t going to cut it any more.
Let’s say, for argument’s sake, that I run a peanut butter e-commerce site. Logic dictates that I will want to rank first in organic search for [peanut butter]. The results from my location look like this:
We can see the same principle applied to the earlier Will Smith query, but with very different results – both in their format and their content.
I may want to rank for [peanut butter] with my e-commerce site, but unless I have a physical store I can use to rank via local listings, the chances look slim. There are a few organic results above the fold (an anomaly these days), but only one brand that actually produces the product. There is a recipe with an accompanying image, however, and a link to more images, so perhaps these formats would be a more appropriate, achievable way to get onto page one.
At the bottom of this search results page, Google actually provides some strong clues about what people are really looking for when they search for peanut butter:
These related searches are more specific and give us a good idea of which topics we should cover on our site. There is a nice variety of different topics here, all of which are worthy of more investigation.
To pick one, we’ll go with the ‘peanut butter ingredients’ route. If I search for [what is in peanut butter?], Google serves the following results:
We can already sense some opportunities for an e-commerce site either to branch out is content strategy to answer questions, or potentially to partner with a site that already ranks well for these queries.
The ‘People also ask’ list is a fantastic resource for users and SEO marketers, but we should be aware of just how dynamic that list is. It take on a concertina effect and expands based on our interactions with it.
Once more, the need to approach SEO in 2017 with an open mind is evident. We can’t control how this list will function at scale; all we can do is put ourselves in the best possible position to answer common questions.
In the screenshot below, there are two examples of how the list changes based on the questions a user clicks on. On the left-hand side, I have clicked on a protein-related question and, therefore, Google provides more protein-based questions below the original list of four. On the right-hand side, I have initially clicked on ‘Is eating peanut butter good for you?’
The ‘People also ask’ box ends up looking completely different in these two instances, which both began with the exact same query.
Note that a lot of similar questions are phrased slightly differently, but Google knows that the underlying meaning is essentially the same. As such, we don’t need to slavishly devote ourselves to answering the exact questions that receive the most searches in order to rank.
This brings with it opportunities and challenges, outlined in the four-step process below.
Four steps to rank via semantic search
We can’t control exactly which queries we will rank for, but we can certainly increase the probability that we will improve our organic visibility if we work through these four stages.
Google provides a lot of useful information via suggested search, ‘People Also Ask’, and related searches. You could use these to collect a list of direct questions that you can be certain people are asking, as a starting point.
Although keyword-level search volumes are impossible to obtain with any serious degree of accuracy now, there are still some useful tools that provide insight into search trends. Google’s own keyword planner is quite limited for SEO nowadays, but you can use PPC-based insights to help shape your content strategy.
There are also tools like Moz’s keyword planner, which are very helpful for shaping broader SEO strategies while still keeping an eye on where the search volume is.
Personally, I find Answer the Public to be a useful guide when trying to figure out all the interrelated questions and pain points consumers have when thinking about a product or service.
Collate a list of all the navigational, informational, and commercial queries related to your site, then sub-categorize them by their semantic links to each other.
From here, you can start thinking about how to structure this to ensure maximum SEO visibility.
Site structure is a fundamental aspect of semantic search performance.
You should think of your products or services as entities that each contain a multitude of connotations and associations. Build those connotations in vertically to cover a range of user needs, and link them to other entities horizontally in the site taxonomy. By mapping keyword groups or common questions to landing pages, you can ensure that each URL on your domain has a defined purpose.
Changes to site structure normally require buy-in from multiple stakeholders, so I would advise visualizing your proposed site taxonomy as early as possible.
How you present this will depend on your intended audience and how they think. For more logical thinkers, Writemaps is a great way to produce simple but effective site structure visualizations.
If you require a more conceptual approach to emphasize semantic relationships, or even the amount of internal link value you want to send to each area of the site, you can use word cluster software like Smartdraw to get your point across.
The next step is to populate your site structure with content that meets user needs. This is an effective way to think about this, because consumer needs and desires remain relatively constant, and the ideal functioning of a search engine will always seek to satiate those underlying motivations. So if you can create content to cover every aspect of the typical consumer journey, you will be rewarded.
Bear in mind what we have seen from the example above, too. Multimedia results are hugely significant, so try to include a range of assets that fit users’ (and Google’s) expectations. Most rank tracking software providers now contain products that allow us to see which types are most prevalent for different types of queries, so use these to guide your efforts.
Measurement has become a significant challenge, viewed through the lens of our old performance indicators like ranking positions, for example. It is very difficult to track individual ranking positions, as they are never static. Search results pages act like living organisms now, so we need to take a broader perspective on measurement.
Track the metrics that matter most to your business, rather than just looking at rankings. The aim should always be to use SEO to affect those metrics anyway, so incorporate them within your campaign tracking.
Moreover, the bigger ranking software companies have created their own metrics to measure SEO visibility which, when combined with what you see in your analytics dashboard, will provide a lot of insight into whether your strategy is working.
We can’t approach measurement like we used to, but we can still tell when SEO is making a positive contribution.
For more on semantic search and its ever-changing impact on the SERP, check out our round-up of five important updates to Google semantic search you might have missed.
What is semantic search? Broadly speaking, it’s a term that refers to a move towards more accurate search results by using various methods to better understand the intent and context behind a search.
Or as Alexis Sanders very eloquently explained it on the Moz Blog,
“The word “semantic” refers to the meaning or essence of something. Applied to search, “semantics” essentially relates to the study of words and their logic. Semantic search seeks to improve search accuracy by understanding a searcher’s intent through contextual meaning. […] Semantic search brings about an enhanced understanding of searcher intent, the ability to extract answers, and delivers more personalized results.”
Google is constantly making tweaks and changes to its documentation and features linked to semantic search. Many of these involve things like structured data and Schema.org, rich results, Knowledge Graph and so on, and the vast majority go unannounced and unnoticed – even though they can make a significant difference to the way we interact with search.
But there are some eagle-eyed members of the search community who keep tabs on changes to semantic search, and let the rest of us know what’s up. To aid in those efforts, I’m rounding up five recent important changes to semantic search on Google that you might not have noticed.
100% of the credit for these observations goes to the Semantic Search Marketing Google+ group (and specifically its founder Aaron Bradley), which is my source for all the latest news and updates on semantic search. If you want to keep in the loop, I highly recommend joining.
Videos and recipes are now accessible via image search
Earlier this week, Google made a telling addition to its documentation for videos, specifying that video rich results will now display in image search on mobile devices, “providing users with useful information about your video.”
A mobile image search for a phrase like “Daily Show Youtube” (okay, that one’s probably not going to happen organically, but I wanted to make the feature work) will fetch video thumbnails in among the grid of regular image results, which when selected, unfold into something like this:
You then need to select “Watch” or the title of the video to be taken to the video itself. (Selecting the image will only bring up the image in fullscreen and won’t redirect you to the video). So far, video rich results from YouTube and Wistia have been spotted in image search.
Google’s documentation for recipes also now features a similar addition: “Rich results can also appear in image search on mobile devices, providing users with useful information about your recipe.” So now you can do more than just stare at a mouthwatering picture of a lasagna in image search – you might be able to find out how it’s made.
Google’s documentation gives instructions on how to mark up your videos and recipes correctly, so that you can make sure your content gets pulled through into image search.
Rich cards are no more
RIP, rich cards. The term introduced by Google in May 2016 to describe the, well, card-style rich results that appear for specific searches have now been removed from Google Developers.
As identified by Aaron Bradley, Google has made changes to its ‘Mark Up Your Content Items’ on Google Developers to remove reference to “rich cards”. In most places, these have been changed to refer to “rich results”, the family of results which includes things like rich cards, rich snippets and featured snippets.
There’s no information as to why Google decided to retire the term; I think it’s usefully descriptive, but maybe Google decided there was no point making an arbitrary distinction between a “card” and a “non-card” rich result.
It may also have been aiming to slim down the number of similar-sounding terms it uses to describe search results with the addition of “enriched search results” to the mix – more on that later.
Google launches structured data-powered job postings in search results
Google has added another item to the list of things that will trigger a rich result in search: job postings.
This change was prefigured by the addition of a Jobs tab to Google’s ‘Early Access and partner-only features’ page, which is another good place to keep an eye out for upcoming developments in search.
— Aaron Bradley (@aaranged) February 9, 2017
Google also hinted at the addition during this year’s Google I/O, when it announced the launch of a new initiative called ‘Google for Jobs’. In a lengthy blog post published on the first day of the conference, Google CEO Sundar Pichai explained the advent of Google for Jobs as forming part of Google’s overall efforts towards “democratizing access to information and surfacing new opportunities”, tying it in with Google’s advances in AI and machine learning.
“For example, almost half of U.S. employers say they still have issues filling open positions. Meanwhile, job seekers often don’t know there’s a job opening just around the corner from them, because the nature of job posts—high turnover, low traffic, inconsistency in job titles—have made them hard for search engines to classify. Through a new initiative, Google for Jobs, we hope to connect companies with potential employees, and help job seekers find new opportunities.”
The new feature, which is U.S.-only for the time being, is being presented as an “enriched search experience”, which is another one of Google’s interesting new additions to semantic search that I’ve explored in full below.
And in a neat tie-in, reviews of employers are now due to be added in schema.org 3.3, including both individual text reviews and aggregate ratings of organizations in their role as employer.
Google introduces new “enriched search results”
Move over rich results – Google’s got an even better experience now. Introducing “enriched search results”, a “more interactive and enhanced class of rich results” being made available across Google.
How long have enriched search results been around? SEO By the Sea blogged about a Google patent for enriched search results as far back as 2014, and followed up with a post in 2015 exploring ‘enriched resources’ in more detail.
However, in the 2014 post Bill Slawski specifically identifies things like airline flights, weather inquiries and sports scores as triggering an enriched result, whereas in its Search Console Help topic on enriched search results, Google specifies that this experience is linked to job postings, recipes and events only.
According to Google:
“Enriched search results often include an immersive popup experience or other advanced interaction feature.”
Google also specifies that “Enriched search enables the user to search across the various properties of a structured data item; for instance, a user might search for chicken soup recipes under 200 calories, or recipes that take less than 1 hour of preparation time.”
Judging by this quote, enriched search results are a continuation of Google’s overall strategy to achieve two things: interpret and respond to more in-depth search queries, and make the SERP more of a one-stop-shop for anything that a searcher could need.
We’ve seen Google increasingly add interactive features to the SERP like new types of rich result, and Google Posts, while also improving its ability to interpret user intent and search context. (Which, as we established earlier, is the goal of semantic search). So in the recipe example given above, a user would be able to search for chicken soup recipes with under 200 calories, then view and follow the recipe in a pop-up, all without needing to click through to a recipe website.
Google makes a whole host of changes to its structured data developer guides
Finally, Google has made a wide-ranging set of changes to its structured data developer guides. I recommend reading Aaron Bradley’s post to Semantic Search Marketing for full details, but here are some highlights:
- Guides are now classified as covering the following topics: structured data, AMP, mobile friendly design
- Structured data has a new definition: it is now defined by Google as “a standardized format for providing information about a page and classifying the page content.” The old definition called it “a text-based organization of data that is included in a file and served from the web.” This one definitely seems a little clearer.
- Twice as many items now listed under “Technical guidelines”, including an explanation of what to do about duplicate content
- There is now less emphasis on the Structured Data Testing Tool, and more on post-publication analysis and testing – perhaps Google is trying to get users to do more of their own work on structured data markup, rather than relying on Google’s tool?
- All content types are now eligible to appear in a carousel.
If you enjoyed this post, don’t miss Clark Boyd’s exploration of what semantic search means today in the wider context of the industry: ‘Semantic Search: What it means for SEO in 2017‘.
When you think of artificial intelligence, images of futuristic robots or memories of bad sci-fi films might come to mind. However, the reality of AI is actually a lot more tame: a friendly search engine, for instance.
But while we type our queries into Google and usually get fairly useful results, the same has not always been true for the information gleaned by scientific researchers.
Although existing resources like Google Scholar and PubMed provide scientists with resources much faster than the methods of old, they don’t always cover the nitty-gritty details that are needed.
Now, a new, free search engine called Semantic Scholar is using AI technology to help these scientists find relevant information much more quickly.
Semantic Scholar has been labeled a game-changer for these professionals, who previously had no way of effectively combing through mountains of dense research. While Google Scholar has a huge database – it has indexed more than 200 million articles to date – it’s lacking in terms of providing access to metadata.
It can help scientists find studies, but it won’t tell them how often a paper or author has been cited. Essentially, it can make a scientist’s job even more difficult because the research tool they’re using isn’t comprehensive.
But Semantic Scholar is different. Developed by Microsoft co-founder Paul Allen in conjunction with his non-profit organization, the Allen Institute for Artificial Intelligence, Semantic Scholar first launched last November. Known as AI2, the non-profit built the engine in collaboration with Allen’s other research organization, the Allen Institute for Brain Science.
Originally launched as a research tool for computer science, Semantic Scholar’s real appeal is its AI-based design.
Instead of simply listing a study’s abstract and bibliographic data, this new search engine is actually able to think and analyze a study’s worth. GeekWire notes that, “Semantic Scholar uses data mining, natural language processing, and computer vision to identify and present key elements from research papers.”
The engine is able to understand when a paper is referencing its own study or results from another source. Semantic Scholar can then identify important details, pull figures, and compare one study to thousands of other articles within one field.
So why is Semantic Scholar a better option?
“Medical breakthroughs should not be hindered by the cumbersome process of searching the scientific literature,” Allen stated in a press release. “My vision for Semantic Scholar is to give researchers more powerful tools to comb through millions to academic papers online, to help them keep up with the explosive growth of science.”
As it stands now, scientists can use other search engine databases as a jumping-off point, but what they find often requires additional research.
The results don’t give the full picture of a study, its variables, or the overall impact. The CEO of AI2, Oren Etzioni, notes that the current options can result in too much information with no real ranking method.
“If you’re dealing with information overload, you want these things to help you cut through the clutter, [and] slice and dice the results.”
Because the search engine uses natural language, it’s able to think and make judgments about which studies are most relevant to a given search.
TechCrunch notes that “it can make intelligent judgements on … which related or cited papers are most relevant, or what other work the current paper has helped lead to… Results are fast, relevant, and easily sorted or drilled down into. For a scientist who frequently consults such articles, this is a huge advance.”
What does this mean for Google Scholar?
AI2 doesn’t want to compete with Google; that would be a fool’s errand, says Etzioni. They just want to provide a better option. “Our goal is to raise the bar” by providing scientists with more effective options to conduct their research, he says.
In fact, many scientists are planning to use both engines to conduct their research, in part because the current state of Semantic Scholar is somewhat limited in comparison to more established engines.
In addition to computer science, Semantic Scholar now covers the neuroscience field and is able to search 10 million published papers. While that sounds impressive, it pales in comparison to Google Scholar’s current database.
The future of Semantic Scholar
Despite its shortcomings, industry professionals see huge potential in Semantic Scholar. Not only is the engine free of charge, but due to its design, it’s more powerful and thorough than other available options.
Developers have already expanded its territory to the biomedical and neuroscience fields, and they intend to keep growing.
Etzioni says that the engine could eventually become a hypothesis engine, guiding scientists to look at the bigger picture or to view a problem from a different angle.
In so doing, it could act like a department head who points out when a method was previously effective, or an area that has remained untested. It could help give scientists direction and result in better quality research.
And even though the engine is still being developed, it’s already been quite successful. Since Semantic Scholar was first launched, 2.5 million people have used the service and have performed millions of searches.
It may still have a long way to go as far as indexing, but the institute hopes to fully expand the engine’s biomedical research library by the end of 2017. By putting AI at the service of the scientific community, Semantic Scholar ensures that only the best and most relevant studies are used. This will, in turn, lead to higher quality and more advanced research — a concept that stands to benefit everyone.
Semantic search. You’ve heard of it, you’ve researched it and you’re probably wondering what to do about it. Black hat, white hat, and everything in between could soon be a thing of the past, as semantic search forces the industry to revert back to the question: What does the user want? It’s a simple concept, but one that has been lost in a whirlwind of advice, speculation, and see-what-sticks techniques. Semantic search gives the industry a chance to go back to basics and provide information rather than force it. Let’s take a look at how to embrace semantics. Think Like […]
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Search is changing. David Amerland begins with this sentence in the foreword of his book Google Semantic Search. If you are a long time user of the Internet and, more specifically, the search engines, you can see how the way we look for information has changed over the past few years. We are at a moment in which semantic search, the ability to put typed searches into context, represents the most accurate option for granting answers. However, before we delve into this new type of search is appropriate to stop and ask the question: What has motivated our change when searching […]
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Please visit Search Engine Land for the full article.
Is it just me, or does it seem like yesterday we barely knew what the word ‘Google’ meant? The evolution of search technology has progressed in leaps and bounds at what seems like lightning speed. It all started with simple keyword searches, where search engines produced results to phrases directed by users. Back then, this was the main approach for search engines to assign rankings to web pages. Fast forward to semantic search, which uses machine intelligence to interpret what the intended meaning of words are so web searches become more relevant. In fact, some of the most used search engines and social […]
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