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A Beginner’s Guide to Smart Search


One of the paradoxes of the Information Age is that we have so much information at our disposal, but we’re not necessarily smarter about it. We have a wealth of data at our fingertips, but that data is not useful to us if we cannot find what we are looking for. The emergence of big data has led to the storage of large amounts of data that is technically available to us, but this data has become so vast that it is almost impossible to find the specific information we are looking for. This led to the emergence of Smart search as a solution to this problem. Read on to find out what smart search is from a beginner’s point of view.

What is smart search?

What sets smart search apart from search engines and more basic technologies is its use of advanced features like machine learning and artificial intelligence. These technologies do the difficult job of filtering out large amounts of information and sorting the wheat from the chaff for you. This involves understanding not only the search terms used, but also factors such as search intent and tangential considerations of the individual user, such as the user’s location and search history.

How does smart search help?

Context is extremely valuable when it comes to determining search intent and finding the most relevant information from a vast mine of data. As an example, consider the search term “Apple”. It could mean the tech enterprise, or it could mean the fruit. Traditional “dumb” search engines have no way of determining what the user’s intention was, and many end up just guessing and returning large amounts of irrelevant search results. This leaves it up to the end user to do the filtering, which can be time consuming and will likely cause many end users to simply go elsewhere.

In comparison, smart search solutions take into account many different factors. If a user has recently researched tech products, a smart search engine can factor that into the search results and make a good guess that the user is looking for the tech company called Apple.

What other technologies is smart search using?

Smart Search also uses Natural Language Processing (NLP) to both improve understanding of users’ search intent and also to find and return the most relevant search results from sets of search results. large data, often disparate and unstructured. Natural language processing is a way to understand human language and apply it to understand search terms and discover search results.

Traditional search engines can rely on the use of Boolean operators, such as AND and OR, to better understand users’ search terms. These are not user friendly, as they force the user to adapt to the search engine, rather than the other way around. If users don’t know about Boolean operators or how to use them, they will have a hard time getting the most relevant results. NLP allows users to write search terms as they would describe them to another person and get them understood, creating a much more user-friendly search experience.

What Kinds of Data Can Smart Search Understand?

Smart research tools can learn to understand many different formats and data structures. This opens up more opportunities for data to be cataloged and made searchable without requiring the data to be pre-sorted into specific formats. Traditional search engines can be very limited in the types of data they can understand, and many rely on relatively primitive technologies such as markup to help identify data in file structures and formats that they don’t understand natively.

Smart search engines can also categorize and categorize these vast amounts of unsorted data into ordered data without manual intervention. An example of this would be identifying that a consistent name across multiple documents represents a person, and by contextualizing that person in the plethora of documents in which they are mentioned, a profile can be developed that includes a person’s organizational affiliations. , location, area of ​​expertise and much more. All of this then enriches the search results for that person, which again takes place without manual intervention and provides more informative search results without additional effort for the end user.

Conclusion

Smart search is a significant improvement over traditional search tools. By incorporating enhancements like machine learning and natural language processing, intelligent search engines can sort and categorize large amounts of disparate data. This data can then be fed into personalized search results for the end user. Users can search in a natural way, with contextual hints and hints used to clarify ambiguous terms without the end user needing clarification.

All of this translates into richer search results and more user-friendly data discovery. Data is at the forefront of many digital businesses. Therefore, making sense of these vast amounts of data can dramatically increase the productivity of businesses and end users who choose to leverage smart search for their search engine needs.