The field of search engines is evolving in recent years, especially due to the emergence of AI applications, which signify a huge leap in search engines nowadays and in the future.
Google, for example, uses a “Knowledge Graph”, which is the target base based on entities rather than key words which enables presenting a “information card” as an answer to search queries which features a bast amount of data semantically related to the entity which the search was conducted on. Google builds the “Knowledge Graph” based on analyzing previous queries and links between web pages, analyzing databases such as Wikipedia, Wikimedia, and the CIA’s The World Factbook, Schema.org and other reliable websites.
Its objective is to create quick answers to users so that they may use this data to solve their query without searching the internet for an answer, thus reducing the search time by presenting the answer in the search page.
For example, searching for the term AI on Google displays the term’s meaning before presenting the search results using the Knowledge Graph.
In these cases, the AI assists the search engine in understanding the person’s natural language by recognizing the meaning related to the query and the information required to provide answers. Another improvement is the development of search engines’ ability to understand the user’s intent behind the search query. The challenging aspect of this issue is derived from the fact that we use search engines for various purposes (shopping, research, data detection, etc.) and since there are many cases in which one key word can bear several meanings, by analyzing clicking patterns and types of content that the user has searched previously, the search engine can leverage machine learning to determine the searcher’s intent.
For example, Google uses BERT (Bidirectional Encoder Representations from Transformers), which is an NLP tool Google uses to better understand the context of the user’s search query and the nuance and connection between the words. This is done by deciphering and studying the users’ interaction with the content. Furthermore, Google uses RankBrain, a machine learning algorithm it developed, which not only assists in detecting patterns in user queries, but also assists the search engine in predicting which search result the user will click on when presented a query not previously entered based on analyzing historical search results. RankBrain is also used for finding synonyms so that user search results can include only the relevant synonyms and not the one entered by the user.
Other search elements rapidly expanded are voice searches and visual searches:
A voice search: with the emergence of “virtual assistants” such as Siri and Alex, voice queries have become increasingly populat among search engines. In 2020, Google reported that 20% of searches via smartphones are voice queries. These have become longer and more complex. In these cases, NLP can assist us in validating the content’s quality, and the search engines’ abilities to [process content of its kind.
Visual search:
every second a sum total of 5000 pictures on social networks such Facebook and Instagram. Hundreds of millions of pictures are uploaded to them on a daily basis. AI machine learning tools enable an analysis of color and shape patterns and attach them to all data on the picture, in order to assist the search engine in understanding the picture and its meaning. For example, the Google search engine is able to not only catalog pictures for picture searches, but also allows users to find other appearances of this picture on the internet, as well as similar pictures of similar themes or color palates and data on the themes displayed in it. Furthermore. The picture search can be reversed, enabling users to search using a picture rather than a text query, using apps such as Google Lens, which will become in the near future a dominant marketing trend in the field of search engines.
Another use of AI that can improve search engine results is the use of machine learning to identify templates that assist in detecting spam or copied content. Machine learning identifies these patterns by learning from similar past incidents to recognize the use of new templates and spam techniques, then label them as problematic.
For example, despite Google still using people to QA its content, using machine learning tools to detect these patterns drastically reduces the amount of manpower this task now requires.
In conclusion, AI technologies such as NLP machine learning are slowly changing the way search engines are finding and rating data. The more users use them, the “smarter” and more accurate will the received results be. However, it is worth noting that machine learning will never be perfect, and that there will always be a need for human workers to perform the fine-tuning and improvements companies make to their search engines.
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