It seems that there’s a lot of misunderstanding surrounding the way that search engine optimization (SEO) and semantic search play together. They aren’t totally separate concepts!
Certainly, SEO and semantic search overlaps, but the key to success lies in finding how to mesh them together, so that they complement each other, rather than simply overlapping.
All too often, I see webmasters and SEO professionals flitting from one tactic du jour or another like a butterfly on meth. Many even consciously separate their marketing from their optimization efforts, as if each can afford them success independently. It just doesn’t work that way, people!
As semantic search capability continues to develop, it will become increasingly important to stop focusing on precise keywords and hoping that the search engines will recognize relevant synonyms. But more importantly, this also increases our ability to provide content designed more for users than search engines.
But how do we do that?
The first necessary step is to define what we adopt as a definition of SEO. I suspect that 10 different SEO pros would have 10 different notions of what SEO entails. Possibilities range from simply achieving high search engine ranking for specific search terms to anything that improves a website’s bottom line.
Each of us has our own idea of what it entails, but at the end of the day, I think it can be generalized as marketing. The tools and tactics we employ may differ, but the goal (for our clients, at least) should be to optimize the overall performance of the site.
The Oxford dictionary defines semantics as “the analysis of word meanings and relations between them.” We’re interested in lexical semantics, although an argument could be made that Google is at least partly interested in logical semantics. Lexical semantics deals with individual words and their aggregate meaning, whereas logical semantics has more to do with their implication.
Like any other optimization effort, there’s a laundry list of technical considerations and strategizing to be done at the outset. Get your house in order before trying to attract more visitors. You wouldn’t wait until a host of dinner guests arrive before giving a thought to what you’ll serve them, right?
Once you feel you’ve got the site ready for both search engines and users, there’s plenty of time to put on your promotion hat and start bringing in traffic.
Stop trying to just salt your content and meta data with keywords, thinking that’s all you need to get to the top of the search results. And that doesn’t mean to just concentrate on synonyms, either. Instead, focus on conveying the concept of your pages.
For instance, suppose you’re writing a piece on jaguars in Brazil. Before, if your article was well written, you might use jaguar, panther, or jungle cat in order to both show some variety and hit some synonyms.
Today, however, you may achieve as much with references such as most feared predator in the Pantanal region or third largest feline in the world. Google can often connect that sort of dots.
What does this achieve for you?
- It enables you to focus more on providing your users with an interesting and informative read, without peppering your copy with jaguar… jaguar… jaguar, ad nauseum.
- It ties your article to other data, broadening the possibilities of matching your content with search terms like “Pantanal predators” or “largest cats”.
- Because semantic search is based largely upon learning algorithms, it provides the algos with more knowledge. Win-win.
You’ll notice I haven’t even mentioned semantic markup. That’s not to say that schema.org or RDFa won’t provide benefits â€“ on the contrary. But they aren’t essential to take advantage of semantic search technology.
Semantic markup can definitely help, I just don’t normally recommend making a major investment in their implementation except for ecommerce sites.
Granted, semantic search is still in its infancy. It has a long way to go. But it’s no longer just a dream.
Semantic search is already in play, to a degree. And its effectiveness will continue to increase as the algos learn more and more about the information in their indices and what that information means.
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