The document discusses a machine learning-based architecture for converting natural language queries into SQL, addressing the challenges users face in data access due to limited SQL knowledge. It outlines rule-based and machine learning approaches, including techniques like LSTM to handle implied data values and the use of Elasticsearch for handling descriptive values. The proposed model demonstrates high accuracy, achieving 91.7% on the IMDB database, thereby enhancing user interaction with database systems.

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Dr. G. Shobha
Professor, CSE Department
RV College of Engineering, Bengaluru - 59
Natural Language to SQL Query conversion using
Machine Learning Techniques on HPCC Systems
Platform

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PRESENTATION CONTENTS
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• Introduction and Motivation
• Components involved in NLP for NL to SQL Conversion
• Rule Based Architecture for NL to SQL conversion
• Machine Learning Based Architecture to Enrich NL for SQl
Conversion
• HPCC Systems Architecture
• Results & Conclusions

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Introduction and Motivation
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Key Factors of NL to SQL
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• Databases serve as the forefront for most systems today.
• Structured query language (SQL) is used to access and manipulate the
data stored in a relational database.
• Most end users have limited knowledge of SQL and thus face
difficulties in accessing such
• Critical to access the data
• Learn the Querying language and understand the various syntax

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Components Involved in NLP for NL to SQL
Components of NLP
NLP
Part of Computer Science and Artificial Intelligence
which deals with Human Languages
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Rule Based Architecture for NL to SQl Conversion
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Rule Based Architecture for NL to SQl Conversion
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Preprocessor
• Tokenizes the natural language input.
• Remove the redundant tokens
• The output of the preprocessor is duplicated
and supplied to two major components
- Entity Recognizer
- Intent Recognizer
Entity Recognizer
• entity extractor
• a classifier
• a filter.
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Rule Based Architecture for NL to SQl Conversion
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Entity Extractor
• uses parts of speech tagging and a date parser to extract important
keywords from the sentence
• strong probable to form relation names, attribute names or data
• These are then fed into a classifier along with the user defined schema
mappings of relation names and attribute names.
Classifier
• The classifier uses various checks such as Direct, Concatenation, N gram,
hypernyms, synonyms to discriminate the keywords into relation names,
attribute names and residual keywords.
Filter
• The residual words are filtered to extract the words that form part of the data items of
the SQL query.
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Rule Based Architecture for NL to SQl Conversion
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Intent Recognizer
• Process of creating a template of the SQL
query by performing checks for each SQL
clause.
• Various techniques such as the context
identification, distance metric, keyword
spotting, grammar rules etc. are applied to
check for the existence of a particular clause.
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Rule Based Architecture for NL to SQl Conversion
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Challenges faced
• Specific Schema
• Identification of partial or implied data values
• Identification of descriptive values
Go To Solution : Machine Learning Techniques for NL to SQL
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Technologies Involved in Machine Learning for NLP to SQL
Feedforward neural networks
Recurrent Neural Networks (RNNs)
• Networks with feedback loops (recurrent edges)
• Output at current time step depends on current input as well
• as previous state (via recurrent edges)
Training RNNs
Problem: can’t capture long-term dependencies due to vanishing/exploding gradients during backpropagation
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Technologies Involved in ML for NLP to SQL
Go To Solution : Long Short Term Memory Model
A type of RNN architecture that addresses the vanishing/exploding gradient problem and allows learning of
long-term dependencies
Recently risen to prominence with state-of-the-art performance in speech recognition, language modeling, translation,
image captioning
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Technologies Involved in Machine Learning for NLP to SQL

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Machine Learning Based Architecture to
Enrich NL for SQl Conversion
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Data Set Extraction
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• Data extracted from RDBMS
• Apache Common CSV Library - used to extract the dataset in the
form of CSV file
• Attributes which contain descriptive values’ (Ex: Experience,
Description. etc) is also provided as input.
• Three separate components work synchronously to extract
maximum latent information from the dataset, which can either
be used to enrich the natural language or be stored to use during
conversion.
Partial and Implied Values
• Pre-processing techniques
• Embedding Layer
• Long Short Term Memory
• Classification of Inputs
Machine Learning for Implied Data Values

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Pre-processing techniques
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Machine Learning for Implied Data Values

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Embedding Layer
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Machine Learning for Implied Data Values

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LSTM Model
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Machine Learning for Implied Data Values

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Proposed Model – Implied Data Values
Classification of Inputs
• The input Natural Language query is tokenized and
split into different sequences.
• Sequences of 1 word (1-gram) up to sequences of n
words (n-gram, where n is determined by the number
of tokens) is considered for prediction.
• The largest sequences and its classification are
considered (i.e., sub-sequences are ignored).
The final, high confidence classifications given by the
LSTM model can be used in multiple ways, couple of
them are outlined below:
• Enrich the Natural Language query
• Store the data values and attribute names
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Elastic Search –Descriptive Values
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Elastic Search
Stop Analyzer : Discards the Stop words
Ex :
Input: Get the doctors with masters degree
Analyzer: Get doctors masters degree
English Language Analyzer:
converts the words of the input query to its
root word.
Ex:
Input: Show all products which are red bikes.
Analyzer: Show all product which road bike
Components of Elastic Search
1. Analyzers
• The extracted CSV file is used to create an index in
Elastic Search.
• Elastic Search’s Bulk API provides the necessary
functions that can create and store large data
simultaneously.

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Proposed Model – Descriptive Values
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Components of Elastic Search
2. Searching through multiple attributes
3. Generation of suitable fieldname-value pair in
WHERE clause
Multiple columns can be searched in Elastic
Search by using “multi_match” keyword
{ “query”:
{ “multi_match”:
{ “query”: input query,
“fields”:[list of descriptive
column names];
}
}
}
WHERE fieldname1 = value1 AND fieldname2 =
value2 AND.… fieldnameN = valueN

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Proposed Model – Descriptive Values
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HPCC Systems Platform
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Key Factors of HPCC Systems
Platform
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Go To Solutions : Synchronous Combination of Hybrid Machine Learning Model,
Elastic Search, WordNet , HPCC Systems Platform
• Highly integrated system environment
- capabilities from raw data processing to high-
performance queries and data analysis using a
common language;
• Optimized cluster approach
- provides high performance at a much lower system
cost than other system alternatives
• Stable and reliable processing environment proven in
production applications for varied organizations over a
15-year period;
• Innovative data-centric programming language (ECL)
• High-level of fault resilience and capabilities
• Suitable for a wide range of data-intensive

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Introduction and Motivation
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Results
Input Natural
Language Query
Enriched Natural
Language Query
Output SQL Query
show all unmarried
customers who are
men
show all single Gender
'male' customers
SELECT * FROM
t_cstmrs WHERE
LOWER( MaritalStatus )
= 'single' AND LOWER(
Gender ) = 'male'
Names of customers
who have graduated
and from germany
or france
FullName Names of
customers who have
Education 'graduate
degree' and from
CountryRegion
'germany' or
CountryRegion 'france'
SELECT
t_cstmrs.FullName
FROM t_cstmrs INNER
JOIN t_ggrphy ON
t_ggrphy.GeographyKey
=
t_cstmrs.GeographyKey
WHERE LOWER (
t_ggrphy.CountryRegion
) = 'germany' OR
LOWER
(t_ggrphy.CountryRegion
) = 'france' ) AND
(LOWER(
t_cstmrs.Education ) =
'graduate degree' )
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Results
get the price of red or dark helmet
get the price of Color 'red' or Color
‘black' ProductSubCategoryName
'helmet'
SELECT ListPrice , Color FROM
t_prdsubcat INNER JOIN t_prds ON
t_prdsubcat.ProductSubCategoryKey =
t_prds.ProductSubCategoryKey WHERE
LOWER( Color ) = 'red' OR LOWER(
Color ) = 'black'
how much does tire tube cost
how much does ProductName ‘road tire
tube’ cost
SELECT ListPrice , ProductName FROM
t_prds WHERE LOWER( ProductName ) =
'road tire tube'
get the orders from new south wales
australia
get the orders from StateProvince 'new
south wales' CountryRegion 'australia'
SELECT t_saldtls.OrderQuantity,
t_ggrphy.CountryRegion, t_
t_cstmrs.FullName , t_ggrphy.StateProvince
FROM t_ggrphy INNER JOIN t_cstmrs ON
t_cstmrs.GeographyKey =
t_ggrphy.GeographyKey INNER JOIN
t_saldtls ON t_cstmrs.CustomerKey =
t_saldtls.CustomerKey WHERE LOWER(
t_cstmrs.StateProvince) = 'new south wales'
AND LOWER( t_ggrphy.CountryRegion ) =
'australia'
show subtotal of orders for helmet
show subtotal of orders for
ProductSubCategoryName 'helmet’
SELECT SUM( t_saldtls.SalesOrderint )
FROM t_prds INNER JOIN t_saldtls
ON t_prds.ProductKey =
t_saldtls.ProductKey WHERE LOWER(
t_prds.ProductName ) = 'helmet'
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Results – Descriptive values
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Select an item with mountain wheel for entry-
level rider.
SELECT * FROM t_prds WHERE t_prds.Description = 'Replacement mountain wheel for entry-level rider.'
Name the items which have pioneering frame
technology as the HQ steel frame.
SELECT t_prds.ProductName FROM t_prds WHERE t_prds.Description = 'The same pioneering frame
technology is used to give you the highest value as the HQ steel frame.'

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Conclusion
• Partial and implied data values in the natural language queries are identified by a trained hybrid
ML model.
• WordNet is also used as a safety net to understand implied data values where the vocabulary of
the input relational database is not expressive.
• Descriptive values are identified with the help of Elastic Search.
• The accuracy of the system is 91.7% on IMDb database
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Acknowledge
Students of RVCE
1. Shubham Phal
2. Yatish H R
3. Tanmay Hukkeri
4. Akshar Prasad
5. Sourabh S Badhya
6. Yashwanth YS
7. Shetty Rohan

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