Paper presentation: Understanding Natural Language Queries over Relational Databases Authors: Fei Li, H. V. Jagadish

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
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3. “Hello Google”
Where is Temple University
“Hello MySql”
Where is CIS Department ?
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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
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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
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6. Proposed method
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• Return authors who have more papers than Bob in VLDB after
2000.

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

9. Proposed Model
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10. Another Example
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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
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12. Experimental Results
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13. Experimental Results
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14. Experimental Results
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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
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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
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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]
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18. References
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narratives. In SIGMOD Conference, pages 673–676, 2012.
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statistical parsing with semantic tractability. In COLING, 2004.
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19. Any question
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Thank you