Intelligent search: shining light on an essential modern technology

Technology has always been a driving force behind radical transformations, and one of the domains where this is most evident is that of online search. From their humble beginnings to the more complex and sophisticated offerings of today, search engines have come a long way. 

One of the most notable developments in search engine technology is the rise of ‘intelligent search’. This term refers to the use of artificial intelligence (AI) and machine learning (ML) to better understand the user intent behind a search. This is accomplished in large part by getting ML models to analyze large amounts of search data and learn from them. Moreover, intelligent search engines can suggest related topics when you carry out searches.

Decoding intelligent search

Intelligent search (also known as AI-powered or cognitive search) refers to the use of artificial intelligence (AI) techniques to improve the quality of search results.

Intelligent search engines are designed to understand the user's intent and the context of the query. This allows cognitive search engines to provide more relevant, contextual, and accurate results. This is in contrast to traditional search engines, which simply match the query terms with documents in their index without any understanding of the user's intent.

There are a number of different AI technologies that can be used for intelligent search, including natural language processing (NLP), machine learning, and semantic analysis. Intelligent search systems can be deployed as stand-alone applications or as part of a larger enterprise search solution.

Intelligent search can be used in a number of different ways, but some common use cases include:

Improving accuracy:

It can help reduce the number of false positives and false negatives in search results. Cognitive search engines do  this by prioritizing context and user intent while indexing results.

Personalization:

Intelligent search can be used to personalize search results for individual users based on their past behavior and preferences.

Recommendations:

Intelligent search can be used to make relevant recommendations about other content or products that might be of interest to the user. The recommendations are based on their current query or interaction with the system.

Traditional search vs Cognitive search 

Traditional search engines are limited by their reliance on keyword matching. This means they can only return results for queries that exactly match the keywords in their index. So, if there are no documents in the index that contain those exact keywords, the traditional search engine will be unable to return any results.

Here are some of the key differences between the two types of search:

Search result ranking:

‍Traditional search engines rank results based on how well they match the query keywords. Intelligent search engines take into account factors such as the user's past search history, and location, to provide more personalized results.

Accurate results at faster speeds:

‍When you run a search on a cognitive search engine, it doesn’t just go out and fetch results from a pre-determined index. Instead, it analyzes the query and looks for semantic and contextual relevance related to the query. Semantic vector search adapts and learns from user behavior to classify queries in a vector space. This allows the search engine to direct the most relevant and accurate content to the searcher. 

‍Document structure and analysis:

‍A cognitive search engine can be trained to understand the document structure for a wide range of document types (including invoices and contracts). Deep learning algorithms enable it to analyse and identify documents that relevant to your industry or business.

How intelligent search technology works 

When a user enters a query into a cognitive search engine, the NLP algorithms analyze the language of the query and break it down into its individual components. The cognitive search engine then compares the meaning of the query with the content of the documents in its index to find the most relevant results.

The beauty of cognitive search is that it can understand complex queries that would be difficult for a traditional search engine to parse. For example, a cognitive search engine could understand a query like “Show me all customer complaints about my product” and return results accordingly.

Let's take a look at the underlying technology that powers smart search.

Content filtering and indexing

Intelligent search indexes content by looking at the relationships between pieces of content, rather than just the individual keywords. This allows for a more natural way of understanding how information is related, making it more effective in finding the right results.

Indexing content in this way also enables intelligent search to understand the context of a query, and not just the individual words. This is especially useful when searching for information on complex topics, or when trying to find answers to questions that are not easily expressed in simple terms.

Intelligent content filtering involves the use of machine learning algorithms that are trained on large data sets. This data set typically consists of web pages that have been indexed by the search engine. The purpose of this training is twofold: first, it allows the algorithm to learn how to recognize patterns in the data; and second, it teaches the algorithm how to recognize objects, topics and concepts from electronic texts. 

Once these processes have been completed, the algorithm becomes “intelligent” and can be used to filter out irrelevant pages from search results.

Understanding user intent 

Intelligent search engines are designed to understand the user's intent. “Intent” refers to the underlying reason why a person has typed in a particular query in the first place. It can be broken down into three categories: factual information, navigation information, and service recommendations.

Factual information includes things like names, addresses, and phone numbers. 

Navigation information includes directions or when the user is trying to find services or places nearby. The user is looking for a specific piece of content but may not know exactly where it is located. 

Service recommendations come from services that are related to the topic of the user's search query. The user is looking for something specific, but may not even know what they need. 

Intelligent search improves the search experience through pattern detection in data. This data can be in the form of documents, web pages, products, service recommendations, etc. Intelligent search parses large data sets to identify patterns and then indexes results based on the relevance of search results. In order to do this, cognitive search engines use a number of methods, including keyword analysis, text analysis and contextual analysis.

• Keyword analysis is used to identify which keywords are being used in a given document or query. By looking at the frequency of each keyword, search engines can begin to develop an idea of the subject matter of the document.

• Text analysis is used to analyze the text within a document in order to identify relationships between words and create ideas about the contents of the document. By looking at how often certain phrases are used, search engines can begin to build up an idea of what topics are being discussed.

• Contextual analysis includes looking at how words within a document are related to one another and analyzing any extra information that may be included with the document (such as location data). 

Analyzing documents

One of the key features of these engines is their ability to parse through large numbers of documents quickly and effectively. Here, ‘document’ is used to refer to any kind of textual content. In order to do this, intelligent search engines use a variety of techniques, such as natural language processing and machine learning.

To start with, the search engine looks at each document as a collection of entities (people, places, things, etc.). It then uses Natural Language Processing techniques to identify the relationships between those entities. It then builds a conceptual map of these relationships, which helps it understand the meaning of documents and how they're related to one another.

This understanding allows the search engine to do more than just match keywords to documents. It can also provide contextual information and related searches based on the concepts in a document. And because it understands relationships between concepts, it can offer suggested searches that go beyond simple keyword matching.

Conclusion — The future of search is smarter

If your business doesn’t have an intelligent search engine on its website, you are most likely leaving money on the table. This is especially true in the smartphone era which has ushered in the urgency for smarter search results. And businesses need to oblige. Not only do intelligent search engines make it easier for people to find what they want, but they also help businesses rank higher in Google searches and increase webpage visits. 

With the advances in AI and NLP-based site search solutions today, platforms like Zevi are helping businesses improve their customer experience and search relevancy. Interested in exploring intelligent search for your business? Reach out to Zevi to get started.