A query is a way of asking a question. Query relaxation and scoping help us offer the user better results based on what they’re searching for. This article will explore what semantic search is and how it works. It also highlights the tools you can use to help with query relaxation.
Contents
Why Query Relaxation?
Query relaxation is the process of making a query more specific by adding more keywords. This happens when you’re searching for something and want to narrow down your results.
For example, if you search for “good restaurants in New York City,” you may get a lot of irrelevant or duplicate results. If instead, you searched for “good restaurants in Manhattan that have vegan options” or even “vegan restaurants in Manhattan,” then it will be easier to find what you are looking for.
What Is Semantic Search?
Semantic search is the process of searching for things instead of keywords. Semantic search is about finding the meaning of words.
For example, if you type in “duck”, it should know that you mean a bird and not a toothbrush or whatever else your mind might try to associate the word with.
How Does Query Relaxation Work?
Query relaxation is the process of converting a query into a different form that makes it easier to search. Query relaxation makes queries easier to understand and easier to search. This applies rules on the basis of how users will phrase their queries.
For example, if you were looking for information on the topic “the best ways to learn to program” and wanted it in an article format on a website, your query might look like this:
- best ways of learning programming in article format?
- Top 5 articles about learning programming?
Query relaxation applies some rules here so that we can narrow down what we’re looking for:
- best ways of learning – Remove this because it seems redundant since we already have “learning” as part of our topic area (topics with more than one word won’t get this rule applied)
How Does Query Scoping Work?
Query scoping is the process of refining a search query by adding additional terms to the query. This can be done to narrow down or expand results.
Query scoping is an essential part of semantic search and is implemented in two ways:
- Query expansion: Adding more terms to a query, such as “New York” or “NYC,” can increase relevance by expanding the scope of the results.
- Query relaxation: Removing certain keywords from your sentence, for example using “NYC” instead of “New York City,” will allow your search engine to match other synonyms that may be relevant in its place.
How Can The Scoped Queries Be Used In Semantic Search?
The first use case of scoped queries is to limit the number of resources you want to explore for your query. For example, if a user searches for “all” books about dogs and wants just 100 books per page, then their query could be “dog AND book LIMIT 100”. This would return only 100 results from their initial search results.
The second use case of scoped queries is when another query relaxation mechanism doesn’t work out as desired or expected. For example:
- A user has a long list of keywords (e.g., “dog”) and performs broad matching by appending them with “book”, but that still doesn’t cover enough ground for them because there aren’t enough results satisfying both keywords at once; i.e., some books have one keyword but not both (e.g., some books are simply titled “Dog Book” while others include both words in the title). In this scenario, using a different version like “
Benefits Of Using Query Relaxation And Scoping As Part Of Semantic Search.
- Semantic search can be more accurate.
- It can be more relevant.
- It can be more efficient.
- More natural.
Semantic search is the process of searching for things, instead of keywords. It’s a method of searching. It uses machine learning and natural language processing to understand what you are looking for. It then delivers results that are more relevant to your query than traditional keyword-based search engines.
But what does this mean? Let’s take an example: if you were to look up “pizza” on Google, you will get thousands of results about pizza restaurants all across the world—from Chicago deep-dish pizzas to New York thin-crust pies. But if someone asked where they could find some “good pizza nearby” on Google Assistant or Siri, would Google Assistant know how to respond? Probably not!
See Also: Google Search Snippet Descriptions Still Can Be Customized On Based On Queries
Conclusion
At the end of this article, we’ve seen how to query relaxation and scoping can help in the semantic search process. Our docs and tutorials on these topics will help you to learn more about them. You can even try building a semantic search application with our API.