Title
Natural Language Querying of Complex Business Intelligence Queries
Abstract
Natural Language Interface to Database (NLIDB) eliminates the need for an end user to use complex query languages like SQL by translating the input natural language statements to SQL automatically. Although NLIDB systems have seen rapid growth of interest recently, the current state-of-the-art systems can at best handle point queries to retrieve certain column values satisfying some filters, or aggregation queries involving basic SQL aggregation functions. In this demo, we showcase our NLIDB system with extended capabilities for business applications that require complex nested SQL queries without prior training or feedback from human in-the-loop. In particular, our system uses novel algorithms that combine linguistic analysis with deep domain reasoning for solving core challenges in handling nested queries. To demonstrate the capabilities, we propose a new benchmark dataset containing realistic business intelligence queries, conforming to an ontology derived from FIBO and FRO financial ontologies. In this demo, we will showcase a wide range of complex business intelligence queries against our benchmark dataset, with increasing level of complexity. The users will be able to examine the SQL queries generated, and also will be provided with an English description of the interpretation.
Year
DOI
Venue
2019
10.1145/3299869.3320248
Proceedings of the 2019 International Conference on Management of Data
Keywords
Field
DocType
business intelligence query, intelligent database systems, natural language interface to databases, nested query
World Wide Web,Computer science,Natural language,Business intelligence,Database
Conference
ISSN
ISBN
Citations 
0730-8078
978-1-4503-5643-5
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Jaydeep Sen1147.12
Fatma Özcan228465.01
Abdul Quamar313311.39
Greg Stager401.69
Ashish Mittal546.49
Manasa Jammi611.38
Chuan Lei7207.54
Diptikalyan Saha820619.99
Karthik Sankaranarayanan9289.36