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Semantic Search for Better Results

If you think about the “Star Trek” computer with its amazing search functions, you are, in fact, thinking of a semantic search engine (Google itself frequently uses this analogy1). Semantic search takes into account additional, meta information about a query to provide more relevant and more content-rich results compared to traditional, blue-URL SERPs. Since 2008, Google, Yahoo!, Bing and Facebook have made efforts to implement semantic search, but as the knowledge graph shows, Google has made the biggest progress.

The drive to relevant results and fewer queries per search brought about the need for semantic search. In 2009, Google started utilizing rich snippets for reviews and urged webmasters to make full use of this technology. Its RDFa support was further extended to images and videos. In 2010, Google applied snippets to the remaining query types, such as shopping sites, recipes, and multinational websites. In 2011, Google, along with Yahoo! and Bing started developing the Schema project2 in order to better equip web developers with markup algorithm.

Semantic search utilizes artificial intelligence natural language processing, also known as machine learning technologies, for better query SERPs. Effectively, they gather together vocabulary and syntax usage data in order to effectively analyze the context of specific words that are used in searches. For example, a search engine will try to interpret “new products” as either “previously unseen,” or as “not second-hand.” The technology further goes after metadata on page structured markup in HTML5. The supported syntaxes by the major search engines are RDFa Lite and microdata.

Semantic searching software can be extremely useful to search engines as it enables them to give accurate website-specific information directly in the SERPs, thus decreasing user journeys into websites. Just consider the enhanced SERPs in Google, which give product prices, playlists, reviews and visuals. Although this is a blessing for users, for webmasters it means getting fewer people to look around, thus lowering site statistics, which, as the market-funded websites know, is extremely detrimental.

Although search engines support Schema markups, they did not abandon RDFs and microdata, as these are widely accepted and supported among web developers. Search engines are further dedicated to using high-quality data only from dependable sources in order not only to provide relevant websites, but to also try to answer user questions. This is done by breaking down the query into relevant parts and analyzing them to determine the context. The interface, then, gives a best guess according to previous results, and measures whether the user was satisfied with it.

Managing different ontologies and vocabularies can be a hard and often inglorious task. To tackle this, the three search engines set up and will try to enforce it as a standard on the web community. This is not necessarily a bad thing, as standards are advantageous to both developers and SEs. Furthermore, it offers tutorials and videos for beginners. It is worth noting that there are additional open source vocabularies3, 4.

The two latest improvements by Google—the Knowledge Graph and the Knowledge Carousel5are its steps to becoming a “knowledge engine” rather than a simple information one. Both of them utilize Google’s database of interconnected real-world items to succinctly summarize long and complicated topics on its sidebar panel and the scrollbar (only for the carousel)6. It is argued that by arming users with more general pre-knowledge before they click on a link, Google increases the quality of traffic and subsequent conversion rates.

Basically, semantic search offers fantastic opportunities for SEO experts as it further contextualizes search queries and enables better user targeting. Its intelligent language recognition software considers vocabulary usage to determine query context.




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