Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases

By Christopher Baik, Hosagrahar Visvesvaraya Jagadish, Yunyao Li. 2019

73 citations recorded by the source corpus.

A critical challenge in constructing a natural language interface to database (NLIDB) is bridging the semantic gap between a natural language query (NLQ) and the underlying data. Two specific ways this challenge exhibits itself is through keyword mapping and join path inference. Keyword mapping is the task of mapping individual keywords in the original NLQ to database elements (such as relations, attributes or values). It is challenging due to the ambiguity in mapping the user's mental model and diction to the schema definition and contents of the underlying database. Join path inference is the process of selecting the relations and join conditions in the FROM clause of the final SQL query, and is difficult because NLIDB users lack the knowledge of the database schema or SQL and therefore cannot explicitly specify the intermediate tables and joins needed to construct a final SQL query. In this paper, we propose leveraging information from the SQL query log of a database to enhance the performance of existing NLIDBs with respect to these challenges. We present a system Templar that can be used to augment existing NLIDBs. Our extensive experimental evaluation demonstrates the effectiveness of our approach, leading up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL query log information.

Subfields: Artificial Intelligence

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Citations in the corpus

Natural language to SQL: Where are we today? (2020, methodology): “[3] have improved the quality of the mapping and inferring join condition by leveraging (co-)occurrence information of SQL fragments from the query log.”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “Rule-based strategies [11, 38] rewrite queries with pre-defined rules, such as DBridge [38], which constructs a dependency graph to model the data flow and iteratively applies transformation rules.”
An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual Learning (2020, background): “Other works are [3, 46, 28, 18, 10, 21].”