Paper Info
Title
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases.
Abstract
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.
Year
DOI
Venue
2019
10.1109/ICDE.2019.00041
2019 IEEE 35th International Conference on Data Engineering (ICDE)
Keywords
DocType
Volume
Databases,Structured Query Language,Deep learning,Task analysis,Natural languages,Semantics,Maintenance engineering
Conference
abs/1902.00031
ISSN
ISBN
Citations 
1084-4627
978-1-5386-7474-1
2
PageRank 
References 
Authors
0.36
32
3
Name
Order
Citations
PageRank
Christopher Baik122.05
H. V. Jagadish2111412495.67
Yunyao Li314411.06