What is Google’s Semantic Search?
Google’s semantic search attempts to improve on the search formula intended to produce relevant search results for web users by creating rules that define a searcher’s intent and the contextual meaning of search terms.
Every language harnesses the power of semantics to define, clarify, process, and change the meaning of words or words in combination. Since language is the tool that the everyday web user employs to scour the boundless frontier of sites and pages on the Internet, it only makes sense that Google’s inimitable search algorithm would begin to evolve just as language does.
So how does Google develop ways to emulate the intricacies of language? Most of what happens at Google offices is a closely guarded secret, but we can certainly make educated assumptions based on the state of current semantic search capabilities and how they fit into indexing and organizing the web.
What is Semantic Search Technology?
Google is not the only company working on a semantic search engine. Specialized database searches and site-search tools also take advantage of semantics to make sure they are operating at their optimal efficiency and customers don’t drop off when they can’t find what they’re looking for.
Semantics is born of thousands of minute neuro-processes that work in conjunction to create a final meaning. Fortunately, those innumerable processes fit fairly neatly into a few underlying concepts that developers use to create quantifiable semantics.
These are features of our world that our brain processes instantly and without effort — which means we usually take them for granted. Computers have a harder time than we do nailing these down.
There are relevant constraints that help us define a word, phrase, or sentence more narrowly so we can produce a reply that makes sense to our interlocutor. We call these restraints context. Search queries are no different – we expect a reply from the search engine that fits the context of the thing or idea we search for. To recreate context, engine developers rely on data and assumptions.
For example, a search for “virtual reality headset” is most likely submitted by a young person or a tech industry professional, an assumption we can make based on the demographics of searchers. A search for “Etta James” is likely submitted by someone older since we can harness data that says the height of the artist’s popularity occurred in the 1960s.
Search results from a semantic search engine can be refined based on these data and assumptions – as long as the search engine understands them. Marketers are already taking advantage of the importance of context in defining searches by focusing efforts on context marketing, wherein marketers match their target markets with demographics reflected by searches.
If you start talking about oil and how it affects the health of every nation, your conversation partner can probably assume that you mean crude oil. But when a search engine sees the keywords oil and health, it may think that you want to know about how olive oil affects your physical health.
An effective semantic search will strive to guess your intent. A successful system is already visible in countless Google SERPs. If you type “What is it called when you think you have a disease but you don’t?” into a search engine, you will get results for hypochondria. Even though keywords like disease are very ambiguous in this sentence, the engine is still able to parse your intent, among other remarkable things.
Variation in language use can be regional, age-specific, industry-specific, or rely on any number of demographics. Semantic search engines must be prepared for language variation and know how it fits into searches. They have to know that people searching for elevators likely live in North America, but those looking for information on lifts are probably looking for UK-related articles.
From city size to climate, economics to local leadership, countless concepts can affect search results based on location. Google does a fantastic job of indexing and prioritizing business-related searches, and its deep integration with the Maps application is a prime example of how location fits into the semantics of a search.
Since we rely on typing queries into a search engine in our mother language rather than code, the semantic search engines have to translate what we say into a form it can understand, then produce results based on what it thinks we mean. There are a few phenomena of language used in our search queries that are difficult to reproduce outside of the human brain.
Search engines can make educated guesses about the user’s intent and offer synonyms for keywords, but subtle differences in meaning can produce irrelevant results. Engines must learn what nuances exist between synonyms and how SERPs should be adjusted to reflect those nuances.
Google semantic search understands that money is a broad concept of currency, even though we use it as an analogue to cash in everyday speech. But type cash into Google and you’ll find some handy places to get payday loans or turn your checks into hard currency. Google knows that while we use the terms interchangeably in everyday life, there’s a subtle but important difference when typing either term into a search engine.
Like money, some keywords and queries represent large concepts with subcategories that can be rolled into one term. Searching for film will provide results based on movies because the Google semantic search formula understands that film is now a concept that is deeply intertwined with movies.
Search engines develop a notion of concept matching over time as their idea of relevance evolves. A user may become frustrated if searching for film directs him or her to a SERP that provides places to buy rolls of film, and semantic search engines must learn to anticipate such frustrations by matching concepts.
Long ago, AskJeeves attempted to simplify web searching by creating an algorithm that answered real questions written in natural language. As a focus, the concept never really caught on, but now more and more users are relying on natural language queries to find answers.
To tackle natural language queries, semantic search engines have to assign purpose not only to unusual terms like prepositions (in, on, around) and articles (a, an, the), but they have to learn how groups of words fit together to create abstract concepts. Observe the following natural language search queries:
- A. I need a place to work out
- B. I need a place to work out of
The only difference between these two queries is the word of. Because of is a preposition, it carries very little in the way of concrete meaning. However, it holds incredible influence over semantics.
Query A provides results for gyms and answer pages from users who have asked similar questions on forums.
Query B gets confused – it can’t really tell if you’re asking for a place to lift weights or a place at which you can perform your job. To us, the addition of the word of clarifies so much of the searcher’s intent, but to the search engine, of is just too abstract to be able to guess accurately what the user wants. You will still see occasional results for leasable office space, however.
Again, relevance will play a large role in helping define natural language searches. The more people search with natural language and click on what they want to find, the more narrowly semantic search engines will be able to define natural language.
Semantics in a Digital Landscape
Users and developers alike must remember that semantics as a digital feature is still very much in its infancy. It wasn’t long ago that all searches were keyword-based and keyword stuffing was a viable SEO option. However, semantic SEO is on the horizon, and smart inbound marketing teams are already ahead of the curve learning how to maximize research potential and produce quality, informative content in line with semantic goals.
Problems with Semantic Search
Semantic search is far from perfect, but it’s certainly not the fault of developers. The human brain is just too complex and powerful for us to understand its processes in full, so until we do, we can’t quantify what it does and turn it into a carbon copy artificial intelligence – which is a scary concept anyway.
The problem of ambiguity, or flexible meaning in a single word, is also something second language learners struggle with. For instance, the word band can mean “a group of people,” “a strap or belt,” or “a frequency interval.” Even if Google semantic search is able to learn which is the most common meaning its searches are seeking, how can it make sure that those searching for other meanings are still able to discover relevant websites?
The early days of Google search meant looking for new ways to game the system for Search Engine Optimization experts. Efforts to stuff keywords and appeal to the search algorithm caused a tidal wave of content to hit the Internet, and that content was hardly useful to users as an information source. Even worse, most of that leftover content still exists today.
Some SEOs are also still married to the idea of paying full attention to search engines rather than the user experience when developing content, so the modern algorithm that focuses on quality content marketing has to deal with old and new content that isn’t optimized for it. Data gets confused and relevance is not always clear, and it’s mostly because of the sheer volume of content available on the web.
Semantic search developers who want their engines to take the reins have to deal with confusion from content saturation. Old, poorly designed pages contain unreadable content from which search engines have to parse data. But those engines can’t learn semantics from content that has no meaning. Until semantic SEO catches on, semantic search technology will have to wade through a sea of content debris.
Answers to Personal Queries
Until artificial intelligence is so close to our own brain chemistry that it can simulate our senses and draw conclusions based on emotional input (which may or may not be impossible), even a semantic search engine won’t be able to answer questions that require thinking on a personal level. Ask Google What is my future like? and you’ll see an endless list of quizzes that “predict your future.”
The results are simultaneously a testament to Google’s ability to computationally understand language and proof that it’s still a robot. Still, the search engine is using semantics to try to guess what you want from it – just try to simplify the search to my future. Semantic search’s capacity to squeeze more meaning out of your search queries will only advance as search technology moves forward.
Is Semantic Search Worth It?
Yes, semantic search, once it has evolved, will provide a search experience not unlike having your own personal assistant. The search will be able to anticipate your needs based on ideologies and linguistic concepts that you harness in everyday life to gather information from human sources.
While a semantic search engine won’t ever be able to answer questions like “Where was that place I put that thing that time?” without invading your privacy, semantic search developers are working toward a concierge-style semantic search that can process more meaningful information in less time.
So keep searching knowing that each time you submit a query and find the result you need, you’re making a sound contribution to a 100% searchable Internet with semantic search.
Great post. Thanks for sharing it.