Understanding Natural Language Queries over Relational Databases

1. Ashis Kumar Chanda
PhD Student
Understanding Natural Language Queries over
Relational Databases
Authors: Fei Li, H. V. Jagadish

2. Contents
• Problem description
• Motivation
• Previous works
• Proposal
• Experiments
• Conclusion
• Criticism

3. “Hello Google”
Where is Temple University
“Hello MySql”
Where is CIS Department ?
Problem description
Understanding Natural Language
Creating queries
Run queries on Relational database

4. Motivation
• Accept natural language questions
• Help non-expert users to get result from database using SQL
• Providing a platform for communication of user and system
• Combining two important research fields (NLP and SQL)
into one application

5. Proposed method
• Transform a natural language to a query tree
• Verifies the transformation interactively with the user
• Translate the query tree into a SQL statement
Components:
• Generate dependency parser (using Stanford Parser[2] )
• Perform parse tree node mapper
• Parse tree structure adjustor
• Interactive communicator
• Query tree translator

6. Proposed method
• Return authors who have more papers than Bob in VLDB after
2000.

7. Proposed method

8. Proposed method
Different types of nodes 
 Grammar of valid parse tree

9. Proposed Model

10. Another Example
Return the conference in each area whose papers have
the most citations

11. Experimental Results
• Effectiveness (the quality of returned result)
• Usability (how much easier is the system)
 Data set: Microsoft Academic Search (MAS)
 Comparison results with MAS website

12. Experimental Results

13. Experimental Results

14. Experimental Results
Usability:

15. Conclusions
• An interactive natural language query interface for RD
• Generates multiple interpretation to find best one
• Actual user experience is gathered to test the model
• Even naïve user can find complex results easily

16. Criticism
• Strong Point:
– The problem statement is really interesting and important
– It tries to make a bridge between system and user
– Applied NLP in analyzing SQL statements
– Node mapping and valid parse tree generation process is
unique

17. Criticism
• Weak Point:
– Users must need to have domain knowledge
– Natural language variety is not discussed in details
– Parse tree selection process is not clear
– Experimental results are not sufficient for supporting their
demand
– Other parameters / analysis should consider
– It is just an extension of previous work, NaLIR [7]

18. References
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• M.-C. de Marne e, B. MacCartney, and C. D. Manning. Generating typed dependency parses from phrase structure parses. In LREC, pages 449–454, 2006.↵
• R. Ge and R. Mooney. A statistical semantic parser that integrates syntax and semantics. In CoNLL, pages 9–16,2005.
• R. J. Kate and R. J. Mooney. Using string-kernels for learning semantic parsers. In ACL, 2006.
• A. Kokkalis, P. Vagenas, A. Zervakis, A. Simitsis, G. Koutrika, and Y. E. Ioannidis. Logos: a system for translating queries into narratives. In SIGMOD Conference,
pages 673–676, 2012.
• D. K¨upper, M. Strobel, and D. R¨osner. Nauda – a cooperative, natural language interface to relational databases. In SIGMOD Conference, pages 529–533, 1993.
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• P. Liang, M. I. Jordan, and D. Klein. Learning dependency-based compositional semantics. In ACL, 2011.
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• A.-M. Popescu, A. Armanasu, O. Etzioni, D. Ko, and A. Yates. Modern natural language interfaces to databases: Composing statistical parsing with semantic
tractability. In COLING, 2004.
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• L. R. Tang and R. J. Mooney. Using multiple clause constructors in inductive logic programming for semantic parsing. In EMCL, pages 466–477, 2001.
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19. Any question

20. Thank you