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 –Intermediate Language –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 1 st Robust System -- Colboy (Parry) late 1960s 1 st 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 Idea: using knowledge trees for NLIDBs. Direction Node Goal Node Easy to revise & maintain connect CA & R-DB Road map for CA dialogue flow Direct CA towards the goal

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

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

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 Idea: There is still big room to do further research. An adaptive conversation-based NLIDB Dynamic knowledge trees

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