Semantic search technology creates challenges and opportunities for businesses retrieving, optimizing and publishing content
In today’s world, businesses have come to expect immediate, relevant answers to their queries, whether on the Internet or searching in-house repositories of data. Receiving these answers provides competitive advantage, allowing companies to leverage information for actionable intelligence. The ability to manage and share relevant data can determine business success or failure.
What is Semantic Search?
The purpose of a search engine is to be a tool for achieving relevant results, and that means well-crafted and designed for a host of strategies. Today’s industry focus is on semantic searches, which are essentially data sifting methods that employ more than simple keyword matches. The word “semantics” appropriately means the study of meaning. A search engine designed for semantic searches considers the searcher’s intent, the contextual meaning of terms, and the relationships between terms. This method of searching began when Google introduced its Hummingbird update in 2013, with the goal of matching the meaning of a query, instead of just matching a few words.
Tony John at Techulator offered this example of a real-world question best answered by semantic searches:
“You walk in to the kitchen and ask ‘It is so hot outside and I am thirsty, can I have something?’. The answer could be anything like ‘Here is some cold milk’, ‘Drink this cold soda,’ and so on. You may not find anything in a kitchen with the label, ‘Drink if you feel hot’ but anything with the labels Milk, Apple Juice, Orange Juice and so on would be just fine. Also, the answer would exclude anything with the label ‘cooking oil’, ‘dish wash solution’, ‘hot coffee’ etc. The answer is provided based on the intent of the person and not based on the words in question matching the labels on the items in the kitchen.”
How Search Engines Use Semantic Search
Search engines generate relevant findings by interpreting the relationship between the term entered by the searcher and the potential results, based on the frequency of linguistic connections in the data. These relationships are enhanced by skilled annotators who tune the selection process by adjusting the search algorithms. Results are also strengthened when the relevant findings of other searchers making similar queries are recorded and ranked. Their findings are then integrated into the algorithms.Semantic searches may create challenges on the other end for companies wishing to have their information promptly retrieved. No longer can a document’s high ranking placement among search results be ensured by simply including a specific keyword in the data. Search tools now look beyond specific words and phrases for their best results. For example, a semantic search for the “biggest city” yields similar top results to the query, “most populated city,” despite the difference between “most populated” and “biggest.” On the other hand, separate searches for the terms “Aglaonema” and “Chinese evergreen,” which are synonyms for the same plant, yield very different results. Search engines may now consider the intent of the searcher, but they still hunt for keywords and phrases.
Some experts suggest that the best approach is to incorporate keywords with a more refined approach to semantic searches. Masha Maksimava at SEO Powersuite explains this strategy by saying:
“The basic idea behind the new-school [Semantic Search] approach is that you shouldn’t be worrying about keywords at all — instead, you should build a comprehensive, original, high-quality resource, and Google will figure out the rest. … Effectively, the best approach to keyword targeting in 2017 is in-between the old-school and the new-school. When you look at your keyword list, you should first and foremost think about the intent of the searcher, and group the keywords that have the same intent into one cluster.”
Conclusion
The extraction of meaning from search queries is a very powerful capability that is having repercussions on content creation and usage. The future of semantic search may involve coordinating even more powerful artificial intelligence than is currently possible with semantic search methodologies. When that happens, the resulting evolution of research, optimization and publishing strategies will change the meaning of search again.
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