Views: 0 Author: Site Editor Publish Time: 2022-08-30 Origin: Site
Does semantic search have a role in your business and marketing plans, and how can you use it to your advantage?
For simple user queries, search engines can reliably find the right content through keyword matching alone.
The query for 'red toaster' can find all products with 'toaster' in the title or description and the color attribute is red.
Add synonyms like maroon for red and you can match more toasters.
But you have to add these synonyms yourself.
This is what semantic search does.
Semantic search attempts to apply user intent and the meaning (or semantics) of words and phrases to find the right content.
It goes beyond keyword matching, using information that may not be immediately present in the text (the keywords themselves), but is closely tied to what the searcher is looking for.
For example, using the query 'sweater' or even 'sweeter' to find a sweater is no problem for keyword search, but using semantic search to query 'warm clothes' or 'How to keep my body warm in winter?' is more suitable.
To understand whether semantic search is right for your business and how you can take advantage of it, you need to understand how it works and the various components that make up semantic search.
Semantic search applies user intent, context, and concept meaning to match user queries to corresponding content.
It uses vector search and machine learning to return results designed to match user queries, even when no words match.
These components work together to retrieve and rank results based on meaning.
One of the most fundamental parts is context.
The context in which a search occurs is important to understanding what the searcher is trying to find.
The context can be as simple as the locale (an American searching for 'football' requires different things than a British person searching for the same thing) or more complex.
An intelligent search engine will use context at both an individual level and a group level.
The impact on outcomes at the individual level is called personalization.
Personalization will use an individual searcher's affinity, previous searches, and previous interactions to return content that is best suited to the current query.
At a group level, search engines can use information about how all searchers interact with search results to rearrange results, such as which results are clicked on most often or even the seasonality of when some results are more popular than others.
This again shows how semantic search brings intelligence to search.
Semantic search can also leverage context within text.
We've already discussed that synonyms are useful in a variety of searches and can improve keyword searches by broadening the matches for your query to related content.
But we also know that sometimes two words are equivalent in one context but not in another.
The ultimate goal of any search engine is to help users successfully complete a task.
This task might be reading a news article, purchasing clothing, or finding a document.
Search engines need to figure out what the user wants to do, or what the user's intent is.
We can see this when searching on an e-commerce website.
When the user enters the query 'jordans', the search will automatically filter to the category 'shoes'.
This indicates that the user's intention is to find shoes, not JORDAN Almonds (which should fall into the 'Food and Snacks' category).
By understanding the user's intent ahead of time, search engines can return the most relevant results rather than cluttering the user with items that are textually matching but irrelevant.
This might make more sense when applying sorting at the top of the search, such as price from low to high.
This is an example of query classification.
Categorizing queries and limiting the result set will ensure that only relevant results appear.
We’ve already seen how semantic search can be smart, but it’s worth taking a closer look at how it differs from keyword search.
Although keyword search engines have also introduced natural language processing to improve the matching between words. By using synonyms, removing pause words, ignoring plurals, etc., but this processing still relies on matching between words.
However, semantic search can return results with no matching text.
This involves a huge difference between keyword search and semantic search, that is, how queries and records are matched.
To simplify things a bit, keyword searches are performed by matching text.
Because of the overlap in text quality, 'soap' will always match 'soap' or 'soapy'.
More specifically, there are enough matching letters to tell the engine that users searching for one will want the other.
The same match also tells the engine that the query 'soap' is more likely to match 'soup' than 'detergent'.
Unless the owner of the search engine tells the engine ahead of time that soap and detergent are equivalent, in which case the search engine will 'pretend' that the detergent is actually soap when determining similarity.
Keyword-based search engines can also use tools such as synonyms, replacement words, or deletion of query terms to help complete this information retrieval task.
NLP and NLU tools such as typo tolerance, tokenization, and normalization also help improve retrieval performance.
While these all help provide better results, they can fall short in terms of smarter matching and concept matching.
Semantic search is a powerful way to improve search quality.
Therefore, the meaning of semantic search has been applied more and more widely.
Often, these search experiences don't always justify the name.
And while there is no official definition of semantic search, we can say that it goes beyond traditional keyword-based search.
It derives user intent based on the meaning of queries and content by incorporating real-world knowledge.
This leads to the conclusion that semantic search is not simply applying NLP and adding synonyms to the index.
Granted, tokenization does require some real-world knowledge about language constructs, while synonyms apply an understanding of concept matching.
However, in most cases they lack the kind of artificial intelligence that is needed for search to rise to a semantic level.
It's this that makes semantic search both powerful and difficult.
Generally speaking, there is an implicit understanding of the term semantic search that there is some level of machine learning involved.
Almost as often, this also involves vector searches.
Vector search works by encoding details about an item into vectors and then comparing the vectors to determine which ones are most similar.
This is also how vector searches usually work.
A machine learning model takes thousands or millions of examples from the web, books, or other sources and then uses this information to make predictions.
Of course, it's not feasible to ask the model to compare them one by one, so the model will encode the patterns it notices in different phrases.
Except in machine learning, the work of language models is not that transparent (which is why language models can be difficult to debug).
These codes are stored in a vector or long list of values.
Vector search then uses mathematics to calculate how similar different vectors are.
Another way to think about the similarity measure made by a vector search is to imagine vectors being drawn.
If you try to plot a vector into hundreds of dimensions, it's very difficult.
If you draw a vector in three dimensions, the principle is the same.
These vectors form a line when drawn, and the question is: which of these lines are closest to each other?
This principle is called vector, or cosine, similarity.
Vector similarity has many applications.
It can make recommendations based on previously purchased products, find the most similar images, and can determine which items best semantically match the user's query.
With the rise of powerful deep learning models and the hardware that supports them, semantic search has become a powerful tool for search applications.
While we've touched on a few different common applications here, there are even more applications using vector search and artificial intelligence.
Even image search or extracting metadata from images can fall under semantic search.
If done correctly, semantic search leverages real-world knowledge, specifically through machine learning and vector similarity, to match user queries to corresponding content.
