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A Beginner’s Guide to Intelligent Search

One of the paradoxes of the information age is that we have so much information available to us, but we are not necessarily any smarter for it. We have access to a wealth of data at our fingertips, but that data is no good to us if we cannot find what we are looking for. The emergence of Big Data has led to vast quantities of data being stored that are technically available to us, but that data has become so vast that it’s almost impossible to find the specific information we are searching for. This has led to the emergence of Intelligent Search as a solution to this problem. Read on to learn what intelligent search is all about from the perspective of a beginner.

What is Intelligent Search?

What sets intelligent search apart from more basic search engines and technologies is its use of advanced features such as machine learning and artificial intelligence. These technologies do the hard work of filtering through large amounts of information and sorting the wheat from the chaff for you. Doing so involves understanding not just the search terms used, but also factors such as search intent and tangential individual user considerations, such as user location and search history.

How does Intelligent Search help?

Context is extremely valuable when it comes to determining search intent and finding the most relevant information from a vast wealth of data. As an example, consider the search term, “Apple”. This could mean the technology company, or it could mean the fruit. Traditional ‘dumb’ search engines have no way to determine what the user’s intent was, and so many end up simply guessing and returning potentially vast amounts of irrelevant search results. This leaves it up to the end-user to do the filtering, which can be a very time-consuming process and one that will probably result in many end-users simply going elsewhere.

By comparison, intelligent search solutions take many different factors into consideration. If a user has been searching for technology products recently, an intelligent search engine can factor that into search results and make a good guess that the user is searching for the technology company called Apple.

What other technologies does Intelligent Search use?

Intelligent search also uses Natural Language Processing (NLP) in order to both improve understanding of user search intent and also to find and return the most relevant search results from large, often disparate and unstructured data sets. Natural language processing is a way to understand human language and apply that to understanding search terms and discovering search results.

Traditional search engines may rely on the use of Boolean operators, such as AND and OR, in order to better understand user search terms. These are not user-friendly, as they require the user to adapt to the search engine, rather than the other way around. If users are not aware of Boolean operators or how to use them, then they will struggle to obtain the most relevant results. NLP allows users to write search terms as they would describe them to another person and have them understood, creating a much more user-friendly search experience.

What kinds of data can Intelligent Search understand?

Intelligent search tools can learn to understand many different data formats and structures. This opens up more opportunities for data to be catalogued and made searchable without requiring that data be sorted into specific formats beforehand. Traditional search engines can be very limited in the types of data that they can comprehend, and many rely on relatively primitive technologies such as tagging to help identify data in file structures and formats that it does not natively understand.

Intelligent search engines can also classify and categorize these vast reams 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 within the plethora of documents they are mentioned in, a profile can be developed that includes a person’s organizational affiliations, location, area of expertise and plenty more. All of this then enriches search results for that person, which again takes place without manual intervention and provides more informative search results at no additional effort for the end-user.

Conclusion

Intelligent search represents a significant improvement over traditional search tools. By incorporating enhancements such as machine learning and natural language processing, intelligent search engines can sort and categorize vast quantities of disparate data. This data can then be incorporated into search results that are customized to the end-user. Users are able to search in a natural way, with cues and contextual hints used to clarify ambiguous terms without the need for clarification by the end-user.

All of this results in richer search results and more user-friendly data discovery. Data is at the forefront of many digital businesses, so making sense of these vast reams of data can provide a huge boost to productivity for the businesses and end-users that choose to leverage intelligent search for their search engine needs.

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