Mr. Majdi Owda, Dr. Zuhair Bandar, Dr. Keeley Crockett

1. Guided Conversational Agents and Knowledge Trees for Natural Language Interfaces to Relational Databases Mr. Majdi Owda, Dr. Zuhair Bandar, Dr. Keeley Crockett The Intelligent Systems Group, Department of Computing and Mathematics, Manchester Metropolitan University.

2. Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

3. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

4. Natural Language Interfaces to Databases • Where the Complexity comes from !! • Past Approaches • Pattern-Matching • IntermediateLanguage • Syntax-Based Family • Semantic-Grammar The Challenge: Creating Simple & Reliable Natural Language Interfaces to Relational Databases.

5. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

6. Conversation Agents • Initial Idea -- Alan Turing (Turing Test) 1950 • First System -- Joseph Weizenbaum (Eliza) 1960s • 1st Robust System -- Colboy (Parry) late 1960s • 1st reusable, general purpose system -- Wallace (Alice) 2000 • MMU (InfoChat-Adam) 2001 Idea: use a guided conversational agent for NLIDBs.

7. Guided Conversation Agents – Why InfoChat • Autonomous general purpose CA • Deals set of contexts • Direct the users towards a goal • Flexible and robust • Converse freely within a specific domain • Extract, manipulate, and store information

8. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

9. Knowledge Trees • Easy to revise & maintain • connect CA & R-DB • Road map for CA dialogue flow • Direct CA towards the goal Direction Node Goal Node Idea: using knowledge trees for NLIDBs.

10. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

11. User Query Agent Response Conversation Manager Response Generation Context Switching & Manage Knowledge Tree Conversational Agent SQL statements Rule Matching Information Extraction Context Script files Relational Database Conversation-Based NLI-RDB Framework • Main components • Conversational Agents • Knowledge Trees • Conversation Manager • Relational Database

12. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

13. Conversation-Based NLI-RDB Interface Tools – Knowledge Tree Builder

14. Conversation-Based NLI-RDB Interface Tools – User Interface

15. Conversation-Based NLI-RDB Interface Tools – User Interface

16. Contents • Introduction • Natural Language Interfaces to Databases • Guided Conversational Agents • Knowledge Trees • Proposed Framework • Developed Interface Tools • Conclusions and Future Work • Q/A

17. Conclusions • Easy and flexible way in order to develop a Conversation-Based NLI-RDB • General purpose framework which can be applied to a wide range of domains • Utilizing dialogue interaction • Knowledge trees are easy to create, structure, update, revise, and maintain • Capability of handling simple and complex queries

18. Current & Future Work • An adaptive conversation-based NLIDB • Dynamic knowledge trees Idea: There is still big room to do further research. 

19. Questions m.owda@mmu.ac.uk