1. Agenda for Presenation  What is NLIDB  What has been done  What is to be done

2. What is NLIDB?  Organized Data  Questions and Answers  Use of SQL  Basically, we are trying to extract data present in SQL databases

3. What is the BIG idea?  Correspondence between relationships and verbs (or Adjectives)  Concept of verb frames to represent the relationships  What is a verb frame??? A frame with a central verb and some arguments

4. How does this idea work?  Some information we need to provide to the NLIDB Understanding the database The different verb frames present  How this information is presented? The 5 files that the developer needs to make

5. ER representation  Example Format %er_rep = { student => { type => 'entity', attribute => [ Student_ID,Name,Date_OF_Birth,Email,Sex,Address,City,State,Pin,Sex,CGPA,Program_ Name,Year_Of_Admission ], primary_key => 'Student_ID', rel_ship => 'register' }, registration => { type => 'relationship', attribute => [ Student_ID,Course_Code,Course_Grade ], primary_key => 'Student_ID,Course_Code', entity => 'student,course_offering' }

6. Verb Frames  Example Format NLexpr=STUDENT$ GET GRADE$ IN COURSE$ ERexpr=register(student._key_,course_offering.gra de,course_offering._key_) NL-Er_Mapping= Verb::GET; SUBJ::STUDENT$::student._key_; OBJ::GRADE$::registration.Course_Grade; PP_IN::COURSE$::course_offering.Course_Name;

7. Definitions  Example Format STUDENT$=pupil,student; GRADE$=grade,marks,percentage;

8. Join Information  Example Format faculty:course_offering=>FACULTY.Faculty _ID=COURSE_OFFERING.Faculty_ID Course_offering:registration=>COURSE_OF FERING.Course_Code=REGISTRATION.C ourse_Code registration:student=>STUDENT.Student_I D=REGISTRATION.Student_ID

9. Database Information  Example Format: IP Address of Database Server = 172.16.9.26 Database Name = academics UserName = root Password = nlidb123

10. What all can the system do?  Understand simple sentences  Understand alternatives of nouns used  Understand alternatives of verbs used  Come up with a probable answer

11. What can it be made to do?  A lot of things Handle complex sentences (Nested Queries) Simple Dialog Modeling (A project on Semantic Completion is done) Improved Adjectives Handling

12. Non NLP stuff which can be done  Distribution mechanism for different domain/database data  Improving database search  Different DBMS

13. The End  Questions?