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Natural Language Queries: What They Are and How They Work

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The rise of artificial intelligence (AI) and machine learning (ML) has enabled multiple businesses to grow. This has introduced new approaches to handling business solutions in a better and more effective way. 

Natural language queries (NLQ) is just one of them. It’s a mechanism that allows individuals to ask queries about data analysis. This kind of communication or exchange of data can be done by using any everyday language. 

NLQ allows users to ask data-related queries so that they can make business decisions. These queries can be typed or even spoken. 

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Once the system gets the query, it uses its machine learning algorithms to process those queries and generate charts and reports. This helps in providing guidance and understanding of the data. It’s also useful for users who don’t have an understanding of programming languages. 

In this post, you’ll learn about various types of NLQs, some basic examples, and, finally, different benefits and challenges. 

Natural Language Queries

In simple terms, a natural language query is an augmented analytics feature that enables a user to type a question in everyday language rather than a data query language like SQL or code to query the data. 

NLQ uses several mechanisms to implement voice interaction, keyword searching, and translations of scripting. Broadly, NLQ can be classified into two different categories: 

1. Search-Based NLQ

This is the primary approach for NLQ. It’s based on searching for questions. These questions are typed into the search boxes, and then these searches are matched with elements in different related databases. 

The tools of this NLQ are mostly embedded with the user experience of business intelligence, which may include dashboards and other majorly used platforms. 

Search-based NLQs usually offer sophisticated and complicated data volumes. But right now, the usage of search-based NLQs is pretty low. 

There are several reasons behind it. The main issue is the lack of knowledge of using the BI tools. So, if someone is new to this tool, he/she would not be getting any guidance on how to use it. 

Also, the users of this tool can go to any data analyst who can teach them the same. But this results in requiring more resources, time consumption, and wastage of the capability of the tool. 

2. Guided NLQ 

Guided NLQ is another approach for natural language queries. It covers the primary challenge of search-based NLQ, as it provides full guidance to the users toward using its tools. 

It also uses a formulation to process user queries and, dynamically, it creates a list of various questions that might be asked by the users. 

Further, it provides various suggestions after covering various levels of filtering and sorting. These features of guided NLQs help the user satisfactorily; that’s why guided NLQs are far more famous than search-based NLQs. 

As discussed above, NLQ is a feature that falls under augmented analytics. So, what could be a better option than Plutora’s augmented analytics tool

Plutora’s augmented analytics tool provides features such as smart data preparation and different methods for statistical analysis. Most importantly, it provides guided NLQ support. 

Textual descriptions of insights from the data can be produced using Plutora’s augmented analytics tool, which may also explain data visualizations. People can better comprehend the stories in their data by having these explanations in plain English rather than requiring a thorough understanding of navigating and interpreting visuals. 

Examples of Natural Language Queries

NLQ is a class of several high-end technologies, producing, processing, and interpreting various regular usage languages such as English, Chinese, Spanish, Hindi, etc. 

Usually, people don’t follow all the rules while speaking any language. But NLQ itself is a machine learning and artificial intelligence-based product, so it uses automation in learning. 

There are various examples of natural language queries available in the market. The most common one is the chatbot service that organizations use to resolve their user queries. 

Another good example is, in organization management, people don’t understand the code language, so they use NLQ to make it easy to explore the data to get some insights from it using general English. Natural language queries are now being extended to two of the more popular tools, named “SQL” and “Excel.” If you belong to a technical background and have hands-on experience working on SQL queries and using different Excel functions, it becomes quite easy for you to analyze and visualize the data. 

But what about the nontechnical audience? Would this still be easy for them to use these tools?

I will say yes, with NLQs now embedded in these tools, nontechnical users can just write the queries in general English, and they can intuitively access the organizational data. 

To know about how SQL API makes use of NLQ, you can refer to this article. And for exploring NLP in Excel, you can check out this one

Sometimes NLQ is confused with NLP and NLU, so let’s discuss each to resolve this conflict. 

NLPs, NLUs, and NLQs

NLP stands for natural language processing. It’s a computational process for evolving the data using methods like artificial intelligence, machine learning, and data science. 

This takes user input as text or voice. It works majorly on unstructured data and converts it into structured data. And then it processes the same with different algorithms. It uses algorithms like the hidden Markov model for the speech-tagging process. Then, it uses recurrent natural networks for different tasks like text/voice data classification, text prediction, etc. 

NLU stands for natural language understanding. This is used for building the algorithms. These algorithms help recognize natural language queries, usually with a focus on full sentences. 

There’s one more term—NLG. NLG stands for natural language generation. It simply uses the templates and then produces the texts that are based on some queries. Over time, natural language generation has collapsed with transformers and other algorithms like NLP. This makes it possible for NLG to produce the results dynamically. 

Benefits of Natural Language Queries 

Self-Serviced and Unique Business Intelligence Tool

NLQ provides guidance whenever asked. This is an immediate assistant tool for all user questions and requires no prior knowledge or technical or coding skills. Users simply have to type the question in the search box and hit enter to get multiple answers for the same. 

Whenever the user clicks on the empty search box, it doesn’t go blank but provides a list of questions that might be asked by the user. So, in short, this is a more user-centric tool than a business intelligence tool itself. 

All Possible Questions Are Already Stored and Understood

Most of the time, all questions are already stored inside the databases with answers. So, it just matches the user query with the elements in the database and returns the most suited one. 

The major benefit is that you don’t need to use any synonyms to make it understand your question—using NLP, it can automatically do the same. Therefore, there’s a limited chance of being misunderstood. 

Simple Process

The process of asking questions to the natural language query tool is simply straightforward. Specifically, with guided NLQs, this is the case. The developer and team have put forth all their efforts to fix the language barriers, and this has decreased the question support complexity. 

Multiple Languages Support

Search-based NLQ and guided NLQ both support various languages that are most commonly used. So, it becomes quite easy for anyone to go through the content availability of NLQs. 

Easy to Embed

It’s quite simple and easy to implement NLQs in any of the local applications. Any user can enjoy the features of NLQs by any software or platform, as it uses BI and is developed using ML. Also, its primary benefit is to be launched by anyone, anywhere, through any source or platform. 

Challenges of Natural Language Queries 

  1. The same words or phrases in English have different meanings. This happens with NLQs as well. Sometimes it gets confused with words and full sentences.
  2. The other challenge is knowing which sentence is the definition and which one is sarcasm. Since both of them look similar, a person can understand the difference, but a machine cannot.
  3. It’s very often that during chatting users use words that don’t make any sense and are incomplete. For example, “Hey!!! Sup?” These kinds of sentences are hard to understand for NLQs, so they provide the nearest results to the user’s questions, which might not be 100% accurate.
  4. Automated corrections help users to improve grammar and spelling. But these cannot be fully responsible for user speech. If a user uses misspelled words, NLQ understands it that way only.
  5. The NLQs are domain specific. For example, an NLQ system for a legal office would be different from an NLQ for a technical business. This will automatically create a difference. For example, we’d use CS in technical terms for computer science, but in legal terms, it stands for company secretary.

Conclusion 

After reading this post, you’re now aware of NLQ, some examples, benefits, and challenges. Let’s summarize with a few takeaways: 

  • Natural language queries are the tools that help in generating the answers for user-asked questions using a database search or ML techniques.
  • NLQ is primarily divided into two types: search-based NLQ and guided NLQ.
  • Guided NLQ is superior to search-based NLQ.
  • NLP is the root mechanism where NLU, NLG, and NLQ are subsets.
  • NLP mainly works for providing the ability for systems to understand human language. It works on the concept of artificial intelligence.

Plutora provides an augmented analytics tool that has NLQ functionality built in.

Gourav Singh
Gourav Bais

Gourav is an applied machine learning engineer skilled in computer vision/deep learning pipeline development, creating machine learning models, retraining systems, and transforming data science prototypes to production-grade solutions.

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