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How to Build AI-Powered Semantic Search Applications

Contents

Introduction
Semantic search vs. keyword search
Semantic search application – what is it?
Txtai
Schema App
Python AST
Sonar Qube
Faiss
Transformers
How to build an AI-powered semantic search application?
Tools required to build AI-powered semantic search applications
Conclusion

 

 

Introduction

 

Semantic similarity has always been a difficult concept. To people, it makes sense. You know that some words are synonyms, and you can say the same sentence in a few different ways – no common words, but the same meaning.

 

Things have always been different for computers. These days, computers are more advanced than ever. How come? Simple. Modern computers evaluate the whole meaning of a sentence and can make connections by themselves. They see the meaning as a whole rather than taking words individually.

 

 

Semantic search vs. keyword search

 

When searching by keyword, you only get the exact matches of the searched terms. This approach is effective if you are sure that exactly these words are used in the searched text.

 

Some keyword search applications have advanced capabilities, such as SeekFast, which not only searches by keyword or phrase but finds sentences in which all the keywords occur in different places or at least one of the search words occurs. Also, the application ranks the results by relevance using multiple criteria, such as the proximity and placement of the keywords in the text, frequency of their occurrence, and more.

 

Despite the good capabilities of this kind of applications, they are still limited by the very principle of keyword search. This type of search fails to return results that do not contain the exact word but use synonyms of the word or phrases that have a similar meaning.

 

With these thoughts in mind, a semantic search will make connections by comparing different sentences or documents according to their meanings. Now, how do you build such semantic search applications? How do you implement artificial intelligence into this concept?

 

 

Semantic search application – what is it?

 

Semantics relates to the meaning of words. It is not all about the actual word but also about synonyms, potential interpretations, associations, etc. All these things will go through a search in terms of relevance.

 

Now, such a search engine will most likely try to find results based on keywords. But then, semantic search engines will also use artificial intelligence and identify meanings. As a result, they can provide solutions that are relevant to the search without actually having the initial keyword.

 

When you use a classic search engine, it is like searching for files on your computer or for a keyword in more documents. The same rule applies when you use programs like Draftable, which find similarities between documents and chunks of text based on actual words.

 

Semantic search engines understand what you mean when you search for something in particular. They are more advanced and will actually understand the request based on artificial intelligence – similar to programs like Affinda, which can extract the required data from documents that are difficult to read.

 

As a direct consequence, result pages will be more accurate, even if the required keyword is not there.

 
 

Txtai

 

Txtai brings in machine learning principles to use data and come up with AI-powered semantic search applications. Most search engines today rely on keywords – this tool makes it easy to identify keywords or results with the same meaning.

 

Models can figure out synonyms and other similar concepts in more than just documents. The same principles apply to images, as well as audio files, among others. It has impressive scalability.

 
 

Schema App

 

Schema App is used for multiple purposes, but it is just as handy when it comes to AI-powered semantic search applications. Whether it comes to research or the necessity to find something, it works on a simple principle – structured data.

 

The tool has an impressive vocabulary and can make associations based on the meaning of a keyword or syntax. The software also helps your website show up in more accurate search results.

 

 

Python AST

 

Python AST is a useful module for processing trees of the Python abstract syntax grammar. AST stands for Abstract Syntax Trees. Once you have the library you want, it will help process the data for more effectiveness in search results. However, you would have to find the data and vocabulary yourself.

 

There are more versions of this module and each of them comes with its own updates and particularities. The newest version is adapted to the latest requirements and terms in grammar.

 
 

Sonar Qube

 

Sonar Qube is similar to Txtai and provides access to static code analyses over about 30 different languages. It offers more features to ensure your code is flawless and it works just as well for semantic search applications.

 
 

Faiss

 

Faiss is practically a library for similarity search. Known for its effectiveness at getting similar meanings in clusters, the tool is mostly used for dense vector applications.

 
 

Transformers

 

Transformers is a top-notch machine learning mechanism for JAX, TensorFlow, and Pytorch. It has gained notoriety for its quick meaning matching mechanisms, as well as its impressive effectiveness.

 
 

How to build an AI-powered semantic search application?

 

Semantic search applications have a clear concept in mind – machine learning over time. Such a search engine will know the meanings of different words and will try to find associations and similarities, even if words or sentences are entirely different.

 

For instance, here is a very useful example.

Your dog looks happy.

This puppy seems joyful.

 

There are no common words in these two sentences. However, they are related. Computers will find associations between them and display both as results because they mean the same thing – no human interaction there.

 

Machine learning is the first and most crucial step in the process when interested in developing an AI-powered semantic search application. The computer requires plenty of data and meanings to make associations.

 

Based on the amount of data they have, computers learn to associate meanings and identify relations and synonyms themselves. Do you know the best part about this concept? No matter what keyword result pages will be based on, there will always be an accurate and relevant result.

 

Even if you cannot get 100% matching queries, results are still relevant. Unfortunately, it is still perfect for developing the ideal application. But with time, applications get better and better, and results will obviously become more accurate.

 

Semantic programs will not just understand keywords but also meanings and contexts. To keep it simple, this type of search application thinks like a human. It starts as a baby with limited knowledge. With time, it gains more and more knowledge.

 
 

Tools required to build AI-powered semantic search applications

 

You can use many programs, software applications, and tools to develop a semantic search application. There are all sorts of libraries; after all, you will need to train your system and help it understand different meanings and contexts.

 

Some of these databases are free; others come with premiums. You need to start somewhere, so you will also have to find a library or database for the initial training. To assimilate all this content, you may need to build a web crawler – lots of programs for such apps, though.

 

Once you get enough information, you will have to process it. You need to ensure that your machine can train with it. The concept is pretty simple to understand – get everything paired. Get a tool to extract the actual code without removing the comments.

 

The new data will be organized in more sets. First, you have the training set. Second, there is the validation set. Finally, the testing set is just as important. Do not clear out all the data – you never know. In other words, keep the initial information, too.

 

Now, to create an AI-powered semantic search application, you must come up with the basic ontology. Such things are usually delivered in OWL files – the optimal format. Such files bring in a plethora of different concepts – also, you will need a Resource Description Framework to create them.

 

Resource Description Firework will store all these details in three different ways. This is the data. Each component will float around the statement. You need to analyze a sentence to help you understand how it works.

 

The cat has big ears.

“The cat” is the subject.

“Has” is the predicate.

“Big ears” is the object.

 

These constructions and analyses will provide the concept for the fundamental ontology. You can rely on different tools to rush the procedure to create such a thing. Then, all the data is structured and pushed into the system during training.

 

If you think about it, it will take ages to develop everything from scratch. Obviously, a small system would work for limited applications – such as businesses. But if you want something more comprehensive, you can use one of the wide variety of pre-trained models on the market.

 

Not only are these models convenient and versatile, but they will also save you plenty of work and time. After all, you already have a base for your application. You can go on from there and extend the amount of knowledge.

 

If you need this app for a specific project, customizing it from scratch is a better idea.

 
 

Conclusion

 

Machine learning is deeply related to artificial intelligence, and such things are slowly becoming part of today’s society, whether you notice it or not. Machines learn new things and try to identify patterns to keep you happy.

 

AI-powered semantic search applications have become incredibly useful these days. The technology can work wonders in numerous industries – from electronic commerce to pharmaceuticals. Some businesses rely on such applications to simplify things, improve performance or increase productivity.

 

In terms of sales and commerce, the technology is just as handy in identifying potential buyers.

 

In theory, the concept is relatively new, especially when compared to other technologies. Building the perfect semantic search application is nearly impossible, but you can get pretty close if you follow some simple rules. Furthermore, technologies these days tend to evolve relatively fast.

 

The progress is there, no doubt about it. Chances are this technology will follow the same trends and become stronger and stronger – but also more useful.

About the author
Lori Wade
Lori Wade is a journalist and content writer from Louisville. Lori creates news and informative articles about HR, recruiting, and employee productivity. You can find her on LinkedIn.

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