Semantic search allows search engine algorithms to understand the meaning and purpose of a user’s query, to interpret it, and to present the desired solution directly.
In order to understand semantic search, we need to take a look at the keyword-based search. In this process, the search engine searches the index for websites which are of high relevance to the keyword being searched for. Depending on the relevance, the search results are placed in a sequence and output. The user decides which result is most relevant to him and retrieves it.
In a semantic search, the algorithm examines sentences and texts based on the relationships of the words to determine what the user might have intended with his search. It then tries to find the answer to the question and display it directly to the user.
An example of a semantic search engine is Wolfram Alpha. If you look for “gross domestic product USA,” the semantic search provides a value of 16.89 trillion US dollars per year. In addition, the user can now choose whether he would prefer to see the nominal or real GDP, thus further specifying his search query.
Google, on the other hand, delivers 10.7 million search results for the same search, but the user must find the answer to his question himself. Therefore, if the semantic search understands the question, then it can shorten the path to obtaining the information.
The semantic search makes it easier for users to search for specific information or shortens the time for researching the correct search results and their evaluation. Ideally, the search engine provides the solution to the question directly, without the user having to search further hits for the desired answer.
However, there are also limitations with regard to semantic search. The result of a semantic search can only be as good as the question asked. The more information users communicate in their search request, the greater the chances of the search engine to deliver good results. For example, if a user searches only for “New York,” it is impossible to guess what they are looking for. Do they want to travel to New York, find the nearest restaurant or see a musical play? However, if the user enters “New York tickets musical plays,” the search engine can return the specific information for that.
Google introduced semantic search with the Hummingbird update in the third quarter of 2013. The search results do not differ significantly from the previous keyword-based search in terms of their layout. But there are different mechanisms at work in the background. Google now prepares the data in the form of knowledge graphs, which allow content to be meaningful and interrelated.
Google is trying to personalize all results for the improvement of the semantic search. Google Plus, Gmail, Google Play, Google Maps, Google Chrome, and other services are used to create an individual user profile. In addition, Google as a search engine has the option to include the previous search behavior of users in the evaluation of their future search queries. However, this may result in a legal problem because of the storing of extensive, user-related data.
The potential effects of search engine optimization is still a hot topic among experts. While some already claim the end of the search engine optimization as we know it, others are dismissing it and rely on the slow development of the semantic search leaving enough time to adjust optimization strategies. If the personalized search is fully implemented, the current website rankings for a particular keyword can be dispensed with, since the SERPs can be different for each user. Nevertheless, there are still many optimization criteria which are already valid now and will remain valid. These include, for example:
Matt Cutts talks about the future of the semantic search in the YouTube video What is the future of semantic search?.