Can Natural Language Descriptions Be Systematically Translated into SQL Queries?

Simplify SQL query building

When faced with human-readable query descriptions, we often rely on heuristics and brainstorming to construct queries. But is there a way to systematically translate these descriptions into SQL queries?

From natural language to SQL

It turns out that there is a correspondence between natural language expressions, logical expressions, relational algebra expressions and SQL expressions. Each table is associated with a predicate that, when populated with the table's column values, is a natural language template for a true proposition.

SQL operators and predicates

Different SQL operators map to logical relationships between predicates. For example:

• R JOIN S is equivalent to row (r ∧ s)
• R WHERE condition is equivalent to row (r ∧ condition)
• R INNER JOIN S ON condition is equivalent to row (r ∧ s ∧ condition)
• SELECT DISTINCT A.C AS D FROM R is equivalent to the row ((A.C = D) ∧ r )

Use aliases and IN clauses

Aliases allow us to rename columns and use them as if they were from a different table. The IN clause selects rows based on whether the subquery results match the specified columns.

Example breakdown

Consider the following natural language query:

Find all (person, liked) pairs, where person is Bob, and Bob likes someone who likes someone who dislikes Ed.

Convert to SQL

Converting this to SQL involves breaking it into logical components and using the appropriate operators:

SELECT DISTINCT l1.liker AS person, l2.liked AS liked
FROM Likes l1
INNER JOIN Likes l2
ON l1.liked = l2.liker
WHERE l1.liker = 'Bob'
AND NOT (l1.liked, 'Ed') IN (SELECT liker, liked FROM Likes)

Conclusion

By understanding the correspondence between natural language predicates and SQL expressions, we can develop a systematic approach to building SQL queries based on human-readable descriptions. This allows us to avoid brainstorming every time and improve query building accuracy and efficiency.

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